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Staff Data Scientist, Finance – Consumption Forecasting

Location: San Francisco or New York | Hybrid (3 days per week in office)

Salary: $240-300k base + bonus + equity (RSUs)

This is a rare chance to own forecasting infrastructure at the center of a high-growth, consumption-based developer platform, one that powers some of the world’s most dynamic applications and scales with every developer and enterprise building on it.

As a consumption-based business, forecasting usage across compute, bandwidth, edge, and storage isn’t a support function. It’s foundational to how we plan infrastructure, revenue, and long-term strategy. This role exists to lead that work at the highest level.

What you’ll own

This is a senior individual contributor role with organization-wide impact. You’ll define forecasting methodology, build systems that scale with a rapidly growing platform, and sit at the intersection of Finance, Infrastructure, Product, and GTM with direct visibility to executive leadership.

• Own production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, and iteration from first principles to live system
• Build forecasting systems that account for usage-based pricing dynamics, consumption patterns, and customer lifecycle across the platform, built for how this business actually works, not retrofitted SaaS models
• Design hierarchical forecasting models across account, cohort, segment, and global aggregate levels, covering operational, quarterly, and long-range planning cycles
• Establish backtesting, monitoring, and explainability standards that make forecast accuracy transparent and defensible
• Build scenario simulation frameworks to evaluate pricing changes, packaging adjustments, and product launches
• Partner with Finance on board-level reporting, with Infrastructure Engineering on capacity planning, and with Product and GTM on adoption curves and usage drivers
• Set forecasting best practices across the broader Data organization

What we’re looking for

• 7+ years in data science, quantitative analytics, or applied statistics at senior or staff level
• Deep expertise in time-series forecasting and statistical modelling in a usage-based or SaaS environment
• Proven track record building and productionizing ML systems at scale
• Strong Python and SQL, with experience on large-scale usage and billing datasets
• Familiarity with probabilistic modelling, hierarchical forecasting, and causal inference
• Experience partnering with Finance or executive leadership on planning cycles
• Comfortable operating autonomously in fast-moving, ambiguous environments

Nice to have

• Background in cloud infrastructure, developer tools, or consumption-based revenue models
• Familiarity with modern data stacks: Snowflake, Delta Lake, dbt, Airflow
• Prior technical mentorship or informal leadership experience

To Apply for this Job Click Here

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Senior Data Scientist – Pricing

London / Leicester | £60,000 – £75,000

This is an exciting opportunity for a Senior Data Scientist to join a highly advanced analytics function focused on pricing innovation. You’ll work on developing cutting-edge machine learning models, leveraging new technologies and data sources to influence commercial strategy and deliver tangible business impact.

The Company

They are a data-driven organisation that has invested heavily in its analytics and pricing capabilities. Their Data Science team sits at the heart of strategic decision-making, helping to improve risk assessment, customer outcomes, and commercial performance. Working within a collaborative environment, you’ll partner with experts across analytics, engineering, and business functions to drive innovation and challenge conventional approaches.

The Role

You will:

• Develop and deploy machine learning models that support pricing and risk management strategies.
• Build predictive models focused on claims outcomes, fraud detection, and key business performance metrics.
• Create and maintain analytical tools used to manage and optimise risk portfolios.
• Identify, assess, and utilise new data sources to improve model performance and business outcomes.
• Engineer new predictive features that can be incorporated into pricing algorithms.
• Take ownership of projects across the full machine learning lifecycle, from exploratory analysis through to deployment and monitoring.
• Collaborate with Data Scientists, Data Engineers, Analysts, and business stakeholders to deliver commercial value.
• Explore emerging machine learning techniques and assess their practical application within the business.

Your Skills & Experience

• Strong commercial experience delivering end-to-end machine learning projects.
• Excellent Python and SQL skills.
• Experience with Azure Machine Learning, Azure Cloud Services, and Git.
• Strong understanding of model deployment, monitoring, evaluation, and optimisation.
• Ability to translate complex business problems into scalable analytical solutions.
• Experience engaging with a range of stakeholders to define requirements and drive successful outcomes.
• Strong communication skills and a collaborative approach to problem-solving.
• Passion for applying advanced analytics and machine learning to real-world business challenges.

How to Apply

To find out more about this opportunity, please apply with your CV and we’ll be in touch to discuss the role in more detail.

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Scientist

Liverpool (Hybrid) | Up to £55,000

Looking to apply advanced analytics and machine learning in a role where your work directly influences business decisions? This is an opportunity to join a well-established consumer-focused organisation investing heavily in its data capabilities. You’ll work on a broad range of projects, turning complex datasets into actionable insights and delivering measurable commercial impact.

The Company

They are a recognised organisation with a large customer base and a growing commitment to data-driven decision making. Their analytics and technology teams play a key role in shaping strategy, improving customer outcomes, and driving operational performance. With continued investment in AI, machine learning, and cloud technologies, they offer an environment where innovation and collaboration are actively encouraged.

The Role

You will be responsible for:

• Collecting, cleaning, and preparing data from multiple sources to ensure quality and accuracy.
• Applying statistical analysis and machine learning techniques to solve business challenges.
• Building, deploying, and maintaining predictive models that deliver tangible value.
• Developing and enhancing AI and machine learning solutions using modern cloud-based tools.
• Creating dashboards and visualisations that communicate insights clearly to stakeholders.
• Working closely with analysts, engineers, and business teams to deliver data science projects.
• Identifying trends, opportunities, and risks through advanced analytics.
• Keeping up to date with the latest developments in data science, AI, and machine learning.

Your Skills & Experience

• Strong commercial experience delivering data science solutions.
• Excellent Python or R programming skills.
• Strong SQL capabilities for data extraction and analysis.
• Experience with forecasting, statistical modelling, and machine learning techniques.
• Knowledge of the full data science lifecycle from model development through to deployment and monitoring.
• Experience working with cloud platforms such as Azure, AWS, or GCP.
• Ability to communicate technical findings to both technical and non-technical stakeholders.
• Passion for using data to solve real-world business problems.

How to Apply

To find out more about this opportunity, please apply with your CV and we’ll be in touch to discuss the role in more detail.

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Scientist

£45,000 – £55,000

Merseyside (3 days a week in office)

This is an excellent opportunity to join a growing Data Science function at a business investing heavily in its data and AI capabilities. You’ll work on high-impact customer and commercial projects, helping to shape how machine learning is adopted across the organisation while gaining exposure to cutting-edge tools and a rapidly evolving data environment.

THE COMPANY

This organisation is investing heavily in its Data and AI capabilities, with a focus on building a scalable Data Science function that delivers real business impact. As Data Science becomes increasingly central to decision-making, you’ll have the opportunity to work on high-profile customer and commercial projects with strong backing from senior leadership. They foster a collaborative, pragmatic culture where Data Scientists are encouraged to move beyond experimentation and deliver solutions that create measurable value. The focus is on deploying models into production and embedding data-driven thinking across the business.

THE ROLE

As a Data Scientist, you will work closely with senior stakeholders, analysts, engineers, and fellow Data Scientists to deliver machine learning solutions across a range of customer and commercial use cases.

Specifically, you can expect to be involved in the following:

• Building and deploying predictive machine learning models
• Developing customer segmentation and customer lifetime value models
• Creating forecasting solutions to support business planning
• Delivering propensity and churn models to improve customer engagement
• Translating complex analytical findings into clear business recommendations
• Collaborating with engineering teams to support model deployment and operationalisation
• Supporting the development of a modern Data Science platform
• Working with cloud-based machine learning tools and technologies
• Engaging directly with stakeholders to understand challenges and define analytical solutions

SKILLS AND EXPERIENCE

The successful Data Scientist will have the following skills and experience:

• Strong commercial experience in Data Science and machine learning
• Advanced Python and SQL skills
• Experience using statistical modelling and predictive analytics techniques
• Knowledge of machine learning algorithms such as XGBoost and related ensemble methods
• Experience delivering projects across the full Data Science lifecycle, from problem definition through to deployment
• Ability to communicate technical concepts clearly to non-technical audiences
• Experience working with customer analytics, segmentation, churn, propensity modelling, forecasting, or related use cases
• Exposure to cloud environments such as Azure or GCP is beneficial
• Experience with Azure ML, Vertex AI, or similar machine learning platforms is advantageous

BENEFITS

The successful Data Scientist will receive the following benefits:

• Salary between £45,000 – £55,000 – depending on experience

HOW TO APPLY

Please register your interest by sending your resume to Majid Latif via the Apply link on this page.

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Scientist

£45,000 – £55,000

Merseyside (3 days a week in office)

This is an excellent opportunity to join a growing Data Science function at a business investing heavily in its data and AI capabilities. You’ll work on high-impact customer and commercial projects, helping to shape how machine learning is adopted across the organisation while gaining exposure to cutting-edge tools and a rapidly evolving data environment.

THE COMPANY

This organisation is investing heavily in its Data and AI capabilities, with a focus on building a scalable Data Science function that delivers real business impact. As Data Science becomes increasingly central to decision-making, you’ll have the opportunity to work on high-profile customer and commercial projects with strong backing from senior leadership. They foster a collaborative, pragmatic culture where Data Scientists are encouraged to move beyond experimentation and deliver solutions that create measurable value. The focus is on deploying models into production and embedding data-driven thinking across the business.

THE ROLE

As a Data Scientist, you will work closely with senior stakeholders, analysts, engineers, and fellow Data Scientists to deliver machine learning solutions across a range of customer and commercial use cases.

Specifically, you can expect to be involved in the following:

• Building and deploying predictive machine learning models
• Developing customer segmentation and customer lifetime value models
• Creating forecasting solutions to support business planning
• Delivering propensity and churn models to improve customer engagement
• Translating complex analytical findings into clear business recommendations
• Collaborating with engineering teams to support model deployment and operationalisation
• Supporting the development of a modern Data Science platform
• Working with cloud-based machine learning tools and technologies
• Engaging directly with stakeholders to understand challenges and define analytical solutions

SKILLS AND EXPERIENCE

The successful Data Scientist will have the following skills and experience:

• Strong commercial experience in Data Science and machine learning
• Advanced Python and SQL skills
• Experience using statistical modelling and predictive analytics techniques
• Knowledge of machine learning algorithms such as XGBoost and related ensemble methods
• Experience delivering projects across the full Data Science lifecycle, from problem definition through to deployment
• Ability to communicate technical concepts clearly to non-technical audiences
• Experience working with customer analytics, segmentation, churn, propensity modelling, forecasting, or related use cases
• Exposure to cloud environments such as Azure or GCP is beneficial
• Experience with Azure ML, Vertex AI, or similar machine learning platforms is advantageous

BENEFITS

The successful Data Scientist will receive the following benefits:

• Salary between £45,000 – £55,000 – depending on experience

HOW TO APPLY

Please register your interest by sending your resume to Majid Latif via the Apply link on this page.

To Apply for this Job Click Here

To Apply for this Job Click Here

Fraud Data Scientist
London (Hybrid, 2 Days per Week) | £55,000 – £65,000

This is an exciting opportunity for a Fraud Data Scientist to take ownership of fraud analytics and fraud prevention within a growing fintech business. You’ll work on high-impact fraud modelling initiatives, helping to shape how the organisation identifies, prevents, and responds to evolving fraud risks while contributing to a business focused on delivering positive outcomes for its customers.

The Company
This fast-growing fintech has developed an alternative approach to consumer lending, focused on providing a more transparent and predictable borrowing experience. Their innovative model is designed to support customers with managing repayments while helping them build stronger financial habits.
With continued growth and investment in data and analytics, the business is strengthening its Decision Science capability and looking for talented individuals who want to make a visible impact. They are also committed to promoting financial education within local communities and have built a strong reputation for their collaborative and supportive culture.

As a Fraud Data Scientist, you will be responsible for developing and improving the organisation’s fraud prevention capabilities through advanced analytics and machine learning.

Key responsibilities include:

• Owning fraud analytics and fraud prevention initiatives across the business.
• Developing fraud detection, fraud scoring, and identity verification models.
• Enhancing fraud decisioning through the incorporation of new data sources and innovative modelling approaches.
• Analysing fraud trends, risks, and insights to identify opportunities for improvement.
• Monitoring model performance and recommending enhancements to optimise outcomes.
• Collaborating with underwriting, product, engineering, and wider business teams to implement fraud solutions.
• Translating analytical findings into clear recommendations that drive fraud prevention strategy.
• Supporting the business in identifying and responding to emerging fraud threats, including identity and first-party fraud.
  

Your Skills & Experience

• Strong commercial experience working within fraud analytics, fraud data science, or fraud strategy.
• Experience within a lending business, fintech, banking, payments, e-commerce, or a similar fraud-focused environment.
• Strong Python and SQL skills are essential.
• Experience developing fraud models or applying advanced statistical analysis to fraud prevention challenges.
• Understanding of fraud detection, fraud scoring, identity verification, or fraud strategy frameworks.
• Ability to measure, communicate, and demonstrate the business impact of fraud initiatives.
• Experience working with cross-functional stakeholders to implement analytical solutions.
• Exposure to AWS SageMaker and Databricks would be beneficial.

How to Apply
If you are interested in this Fraud Data Scientist opportunity and would like to learn more, please apply with your CV for immediate consideration.

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Scientist

Merseyside (3 days per week) | Up to £55,000

Looking to make a tangible impact with Data Science in a business that is investing heavily in data and AI? This is an opportunity to join a growing team at a pivotal stage in its transformation, helping to build scalable machine learning solutions that drive real commercial outcomes. You’ll work closely with senior stakeholders and play a key role in shaping how Data Science is embedded across the organisation.

The Company

They are a well-established consumer-focused organisation undergoing a significant data and AI transformation. With strong investment in technology, analytics, and customer insight capabilities, they are building a modern Data Science function focused on delivering measurable business value.

The team is moving beyond ad hoc analytics towards scalable, production-ready machine learning solutions. Data is at the heart of strategic decision-making, creating an exciting environment for a commercially minded Data Scientist to make an impact.

The Role

As a Data Scientist, you will work closely with Data Science leadership, engineers, analysts, and business stakeholders to develop and deploy machine learning solutions across a range of customer and commercial use cases.

Responsibilities include:

• Building, validating, and deploying predictive and machine learning models
• Developing customer segmentation and customer lifetime value models
• Supporting forecasting initiatives to improve commercial planning
• Delivering targeted customer and personalisation use cases
• Working across the full Data Science lifecycle from problem definition through to production deployment
• Collaborating with technical and non-technical stakeholders to translate business challenges into analytical solutions
• Contributing to the development of a scalable Data Science platform and best practice framework
• Presenting insights and recommendations clearly to business audiences

Your Skills & Experience

• Strong commercial experience in Data Science and machine learning
• Excellent Python skills, with SQL expertise
• Experience delivering end-to-end Data Science projects from development to deployment
• Knowledge of statistical modelling, forecasting, and predictive analytics techniques
• Experience communicating technical concepts to non-technical stakeholders
• Ability to work in a collaborative, business-focused environment
• Exposure to cloud technologies such as GCP or Azure is advantageous
• Experience with Vertex AI, Azure ML, or similar machine learning platforms is beneficial

What They Offer

• Salary up to £55,000
• Hybrid working with 3 days per week in Knowsley
• Opportunity to join a growing Data Science team at an early stage of its journey
• Exposure to high-profile customer and commercial projects
• Significant opportunity to influence tooling, processes, and best practice
• Ongoing professional development and career progression opportunities
• Inclusive and collaborative working environment

How to Apply

• If you’re a Data Scientist who enjoys solving real business problems with data and wants to help shape a growing machine learning capability, apply today to find out more.

To Apply for this Job Click Here

To Apply for this Job Click Here

Lead Data Scientist
Base salary: $140,000 – $150,000

Overview

A leading enterprise organisation is seeking a senior-level Data Scientist to lead the development, validation, and implementation of advanced quantitative models in a highly regulated environment. This role will serve as a technical leader, partnering with business and executive stakeholders to identify financial opportunities, quantify outcomes, and drive data-driven decision making.
The successful candidate will bring deep expertise in risk and financial modelling, strong statistical and mathematical foundations, and the ability to independently deliver complex analytics projects from concept through deployment and governance review.

Key Responsibilities

• Design, develop, validate, and monitor advanced predictive and risk models.
• Analyse large-scale financial, transactional, and customer datasets.
• Apply machine learning and statistical techniques to solve complex business problems.
• Translate model insights into measurable financial impact and business recommendations.
• Lead analytics initiatives from problem definition through implementation and performance tracking.
• Present technical findings and strategic recommendations to senior leadership and executive stakeholders.
• Support model governance activities, regulatory reviews, and external audit processes.
• Collaborate with cross-functional teams to ensure models are aligned with business objectives and compliance requirements.
• Provide technical guidance and mentorship to peers and junior team members.

Required Qualifications

• 8+ years of experience in Data Science, Quantitative Analytics, Risk Analytics, or a related discipline.
• Advanced proficiency in Python and SQL.
• Strong expertise in data modelling, statistical analysis, and quantitative problem solving.
• Experience developing and evaluating gradient boosting and other predictive modelling techniques.
• Proven ability to independently own and drive complex analytical projects.
• Exceptional communication and presentation skills, including experience presenting to executive audiences.
• Demonstrated experience defending models during audits, reviews, or governance assessments.
• Background in financial or risk modelling within a regulated industry.

Required Domain Experience

Candidates should have experience in one or more of the following areas:

• Credit risk modelling
• Financial forecasting and modelling
• Loss estimation and assessment
• Probability of Default (PD) modelling
• Risk scorecard development
• Payment and behavioural modelling
• Credit attribute modelling
• Banking, lending, consumer credit, mortgage, or credit bureau data
• Financial impact analysis
• Risk analytics within regulated financial institutions

Preferred Qualifications

• Bachelor’s degree in a quantitative discipline such as Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field.
• Master’s degree in Data Science, Statistics, Applied Mathematics, Economics, or a similar quantitative discipline preferred.
• Advanced academic training with significant mathematical and statistical coursework.

Work Model / Location / Engagement Details

• Hybrid work arrangement requiring onsite attendance four days per week (Monday through Thursday).

To Apply for this Job Click Here

To Apply for this Job Click Here

Lead Data Scientist
Base salary: $140,000 – $150,000

Overview

A leading enterprise organisation is seeking a senior-level Data Scientist to lead the development, validation, and implementation of advanced quantitative models in a highly regulated environment. This role will serve as a technical leader, partnering with business and executive stakeholders to identify financial opportunities, quantify outcomes, and drive data-driven decision making.
The successful candidate will bring deep expertise in risk and financial modelling, strong statistical and mathematical foundations, and the ability to independently deliver complex analytics projects from concept through deployment and governance review.

Key Responsibilities

• Design, develop, validate, and monitor advanced predictive and risk models.
• Analyse large-scale financial, transactional, and customer datasets.
• Apply machine learning and statistical techniques to solve complex business problems.
• Translate model insights into measurable financial impact and business recommendations.
• Lead analytics initiatives from problem definition through implementation and performance tracking.
• Present technical findings and strategic recommendations to senior leadership and executive stakeholders.
• Support model governance activities, regulatory reviews, and external audit processes.
• Collaborate with cross-functional teams to ensure models are aligned with business objectives and compliance requirements.
• Provide technical guidance and mentorship to peers and junior team members.

Required Qualifications

• 8+ years of experience in Data Science, Quantitative Analytics, Risk Analytics, or a related discipline.
• Advanced proficiency in Python and SQL.
• Strong expertise in data modelling, statistical analysis, and quantitative problem solving.
• Experience developing and evaluating gradient boosting and other predictive modelling techniques.
• Proven ability to independently own and drive complex analytical projects.
• Exceptional communication and presentation skills, including experience presenting to executive audiences.
• Demonstrated experience defending models during audits, reviews, or governance assessments.
• Background in financial or risk modelling within a regulated industry.

Required Domain Experience

Candidates should have experience in one or more of the following areas:

• Credit risk modelling
• Financial forecasting and modelling
• Loss estimation and assessment
• Probability of Default (PD) modelling
• Risk scorecard development
• Payment and behavioural modelling
• Credit attribute modelling
• Banking, lending, consumer credit, mortgage, or credit bureau data
• Financial impact analysis
• Risk analytics within regulated financial institutions

Preferred Qualifications

• Bachelor’s degree in a quantitative discipline such as Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field.
• Master’s degree in Data Science, Statistics, Applied Mathematics, Economics, or a similar quantitative discipline preferred.
• Advanced academic training with significant mathematical and statistical coursework.

Work Model / Location / Engagement Details

• Hybrid work arrangement requiring onsite attendance four days per week (Monday through Thursday).

To Apply for this Job Click Here

To Apply for this Job Click Here

Lead Data Scientist
Base salary: $140,000 – $150,000

Overview

A leading enterprise organisation is seeking a senior-level Data Scientist to lead the development, validation, and implementation of advanced quantitative models in a highly regulated environment. This role will serve as a technical leader, partnering with business and executive stakeholders to identify financial opportunities, quantify outcomes, and drive data-driven decision making.
The successful candidate will bring deep expertise in risk and financial modelling, strong statistical and mathematical foundations, and the ability to independently deliver complex analytics projects from concept through deployment and governance review.

Key Responsibilities

• Design, develop, validate, and monitor advanced predictive and risk models.
• Analyse large-scale financial, transactional, and customer datasets.
• Apply machine learning and statistical techniques to solve complex business problems.
• Translate model insights into measurable financial impact and business recommendations.
• Lead analytics initiatives from problem definition through implementation and performance tracking.
• Present technical findings and strategic recommendations to senior leadership and executive stakeholders.
• Support model governance activities, regulatory reviews, and external audit processes.
• Collaborate with cross-functional teams to ensure models are aligned with business objectives and compliance requirements.
• Provide technical guidance and mentorship to peers and junior team members.

Required Qualifications

• 8+ years of experience in Data Science, Quantitative Analytics, Risk Analytics, or a related discipline.
• Advanced proficiency in Python and SQL.
• Strong expertise in data modelling, statistical analysis, and quantitative problem solving.
• Experience developing and evaluating gradient boosting and other predictive modelling techniques.
• Proven ability to independently own and drive complex analytical projects.
• Exceptional communication and presentation skills, including experience presenting to executive audiences.
• Demonstrated experience defending models during audits, reviews, or governance assessments.
• Background in financial or risk modelling within a regulated industry.

Required Domain Experience

Candidates should have experience in one or more of the following areas:

• Credit risk modelling
• Financial forecasting and modelling
• Loss estimation and assessment
• Probability of Default (PD) modelling
• Risk scorecard development
• Payment and behavioural modelling
• Credit attribute modelling
• Banking, lending, consumer credit, mortgage, or credit bureau data
• Financial impact analysis
• Risk analytics within regulated financial institutions

Preferred Qualifications

• Bachelor’s degree in a quantitative discipline such as Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field.
• Master’s degree in Data Science, Statistics, Applied Mathematics, Economics, or a similar quantitative discipline preferred.
• Advanced academic training with significant mathematical and statistical coursework.

Work Model / Location / Engagement Details

• Hybrid work arrangement requiring onsite attendance four days per week (Monday through Thursday).

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Scientist

Liverpool | Up to £55,000 + Benefits

Join a growing Data Science team within a large UK organisation investing heavily in Data & AI. This is an opportunity to work on customer-focused machine learning projects, help scale Data Science capabilities, and deliver models that create real commercial impact.

The Company
They are building a modern Data Science function as part of a wider data transformation programme. With increasing investment in customer analytics and personalisation, the team is focused on developing scalable solutions that support business growth. The culture is collaborative, pragmatic, and centred on delivering value through production-ready machine learning.

The Role

• Build and deploy predictive and statistical models
• Develop customer segmentation, customer lifetime value, and churn models
• Deliver forecasting and customer analytics solutions
• Work across the full Data Science lifecycle from problem definition to deployment
• Partner with analysts, engineers, and business stakeholders
• Present insights and recommendations to non-technical audiences
• Support the development of Data Science best practices and capabilities

Your Skills & Experience

• Strong commercial Data Science experience
• Excellent Python and SQL skills
• Experience with machine learning and statistical modelling
• Proven experience deploying models into production
• Strong stakeholder management and communication skills
• Exposure to Azure or GCP is advantageous
• Experience with customer analytics or forecasting is beneficial

What They Offer

• Salary up to £55,000, with flexibility for exceptional candidates
• Hybrid working with 3 days per week onsite in Liverpool
• Exposure to high-impact machine learning projects
• Opportunity to help shape a growing Data Science function
• Staff discount, pension, wellbeing support, life assurance, and additional benefits

How to Apply
If you’re a Data Scientist with strong Python, SQL, and machine learning experience looking to make a tangible business impact, please apply today.

To Apply for this Job Click Here

To Apply for this Job Click Here

Data Science Engineer
London 2-3 days

Join a newly established R&D function building data-driven and AI-enabled products. This is a hands-on role for someone who enjoys combining Data Science and Data Engineering to create scalable data solutions with real business impact.

The Company
They are investing heavily in data and AI, with a small multidisciplinary team focused on building analytical products from concept through to production. You’ll work in a collaborative environment with plenty of ownership and exposure to cutting-edge technologies.

The Role

• Build scalable data pipelines using Microsoft Fabric and Databricks
• Develop machine learning solutions across forecasting, anomaly detection, recommendation systems, and information extraction
• Work with structured, semi-structured, and document-heavy datasets
• Create reusable data assets and analytical products
• Contribute to testing, APIs, CI/CD, and production deployments
• Partner with stakeholders to solve business challenges through data

Your Skills & Experience

• Strong experience across both Data Science and Data Engineering
• Hands-on expertise with Microsoft Fabric and Databricks
• Advanced Python and SQL skills, with PySpark desirable
• Experience building data pipelines and machine learning models
• Knowledge of NLP, entity resolution, or document processing is advantageous
• Strong communication skills and commercial awareness

To Apply for this Job Click Here

To Apply for this Job Click Here

Senior Data Scientist
$120,000-$140,000

Overview

A venture-backed, early-stage technology company is seeking a Data Scientist to lead the development and deployment of advanced measurement, experimentation, and causal inference solutions. This individual will play a critical role in shaping core product capabilities, validating model performance with customer data, and helping translate analytical insights into business impact.
The ideal candidate combines strong quantitative expertise with the ability to communicate effectively across technical, business, and customer-facing environments. This is a highly entrepreneurial opportunity for someone excited about building systems, processes, and products from the ground up.

Key Responsibilities

• Own the design, testing, and optimisation of models that drive product recommendations and insights.
• Translate causal inference and experimentation methodologies from research concepts into production-ready solutions.
• Validate model effectiveness using real-world customer data.
• Lead a critical modelling workstream from development through deployment.
• Partner directly with customers during pilot programmes and proof-of-concept engagements.
• Collaborate with cross-functional teams to improve analytical capabilities and product outcomes.
• Contribute to the continued evolution of the company’s data science and measurement strategy.

Required Qualifications

• PhD strongly preferred; Master’s degree considered in Statistics, Economics, Computer Science, Data Science, or another quantitative discipline.
• Experience deploying causal inference and experimentation solutions in production environments.
• Hands-on expertise with methodologies such as:
  • Synthetic Control
  • Difference-in-Differences (DiD)
  • Double Machine Learning (DML)
• Experience designing and analysing A/B tests and experimentation frameworks.
• Strong Python development and model-building experience.
• Demonstrated ability to build new capabilities, products, frameworks, or functions in emerging or fast-paced environments.
• Excellent communication skills with the ability to explain complex concepts to both technical and non-technical audiences.
• Comfortable interacting with customers and external stakeholders.

Preferred Qualifications

• Background in marketplace, technology, analytics, retail, consumer products, or related data-intensive industries.
• Experience building data ingestion pipelines and data infrastructure.
• Interest or demonstrated potential in product leadership, product strategy, or future product management responsibilities.
• Previous startup or early-stage company experience.

To Apply for this Job Click Here

To Apply for this Job Click Here

Senior Data Scientist

Remote (US) | Up to $190,000 Base + Benefits

A rapidly growing healthcare technology company is hiring multiple Senior Data Scientists to help build and scale machine learning products that directly improve patient access to clinical research and healthcare innovation.

This is a highly visible opportunity for someone who wants to own machine learning models from development through deployment and monitoring while partnering closely with Product, Engineering, and business stakeholders. The team is investing heavily in data science as a core strategic function and is looking for individuals who enjoy solving complex real-world problems through predictive modeling and statistical rigor.

What You’ll Be Working On

• Building predictive models that influence healthcare decision-making
• Developing patient matching and recommendation systems
• Forecasting enrollment and engagement outcomes
• Designing and evaluating machine learning experiments
• Monitoring and improving production model performance
• Partnering with engineering teams to productionize solutions
• Influencing product strategy through data-driven insights

What We’re Looking For

• 5+ years of Data Science experience
• Strong Python and SQL skills
• Experience building and deploying predictive models
• Statistical modeling expertise
• Machine learning production experience
• Strong experimentation and analytical thinking skills
• Ability to communicate complex findings to non-technical stakeholders

Preferred Experience

• Healthcare, life sciences, pharma, insurance, or other regulated industries
• Forecasting, Bayesian methods, or advanced statistical modeling
• Experience monitoring and improving production models
• Startup or high-growth environment experience

Why This Role?

• Fully remote opportunity
• Up to $190,000 base salary
• Meaningful healthcare impact
• Strong leadership visibility
• Opportunity to influence product direction
• End-to-end ownership of machine learning initiatives
• Join a business that has proven product-market fit and is investing heavily in growth

If you’re a Data Scientist who enjoys building models that make it into production and drive measurable business outcomes, we’d love to hear from you.

Apply today or reach out directly for a confidential discussion.

To Apply for this Job Click Here

To Apply for this Job Click Here

Product Owner, Knowledge Graphs

Amsterdam (Hybrid) | €70,000 – 75,000 + Benefits

Are you passionate about turning complex legal knowledge into intelligent, scalable digital products?

This is an opportunity to join a forward-thinking organisation at the intersection of LegalTech, Data, and AI, where you will shape the roadmap for a knowledge graph platform that supports legal professionals in making faster, better-informed decisions.

The Company

They are a well-established professional services organisation investing heavily in data and AI-driven innovation. Their teams work on complex information challenges, developing products that transform how knowledge is captured, connected, and consumed.

With a strong focus on emerging technologies, they are building advanced knowledge management capabilities that enable better search, discovery, and insight generation. This role sits within a growing product function with significant visibility and impact across the business.

The Role

As Product Owner, you will lead the development and evolution of a knowledge graph platform designed to connect legal data, documents, expertise, and processes.

Key responsibilities include:

• Own and prioritise the product backlog for knowledge graph and semantic data initiatives
• Translate business and user needs into clear product requirements and user stories
• Work closely with data engineers, knowledge engineers, software developers, and legal subject matter experts
• Define and communicate product vision, roadmap, and delivery priorities
• Drive adoption of graph technologies to improve search, discovery, and knowledge management capabilities
• Balance stakeholder requirements with technical feasibility and strategic objectives
• Monitor product performance and user feedback to inform future enhancements
• Support the delivery of AI and data products that leverage structured knowledge assets

Your Skills & Experience

• Strong commercial experience as a Product Owner, Product Manager, or similar product leadership role
• Experience delivering data, AI, analytics, or knowledge management products
• Understanding of knowledge graphs, semantic technologies, ontology management, or graph databases such as Neo4j
• Ability to engage effectively with both technical and non-technical stakeholders
• Experience gathering requirements and translating them into actionable product roadmaps
• Strong prioritisation and decision-making capabilities
• Familiarity with Agile delivery methodologies and product development best practices
• Interest in LegalTech, professional services, enterprise knowledge management, or information retrieval

What They Offer

• Salary of €70,000 – 75,000 depending on experience
• Performance-related bonus
• Comprehensive benefits package
• Hybrid working model based in Amsterdam
• Exposure to cutting-edge AI, data, and knowledge graph technologies
• Clear career progression within a growing product and innovation function
• Opportunity to influence the direction of a business-critical platform

How to Apply

If you are a Product Owner with experience in knowledge graphs, semantic technologies, or data-driven products and are looking to make a meaningful impact within an innovative legal environment, apply today to learn more.

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Director of GTM Data Science

Overview

A growing technology organisation is seeking a senior data leader to define and execute the strategy for Go-To-Market analytics and data science. Reporting into the head of data, this role will lead a team of analysts and data scientists while partnering closely with executive stakeholders across revenue-generating functions.
The successful candidate will be responsible for establishing data-driven decision-making frameworks, governing key business metrics, driving predictive modelling initiatives, and enabling sustainable growth through advanced analytics and experimentation.

Key Responsibilities

Strategic Leadership & Data Strategy

• Partner with senior leaders across sales, marketing, revenue operations, customer success, and finance to align analytics priorities with business growth objectives.
• Develop and own a multi-year GTM analytics and data science roadmap focused on acquisition, retention, expansion, and operational effectiveness.
• Establish governance standards and definitions for critical business metrics, including customer acquisition, customer value, conversion performance, retention, and funnel analytics.
• Translate complex business questions into structured analytical initiatives and prioritise work based on strategic impact and return on investment.
• Influence organisational decision-making through data-driven insights and recommendations.

Data Science & Advanced Analytics

• Provide technical leadership for predictive and statistical modelling initiatives, including:
  • Customer lifetime value modelling
  • Lead and prospect scoring
  • Attribution analysis
  • Marketing effectiveness measurement
  • Sales prioritisation and propensity modelling
• Guide experimentation programmes using A/B testing, causal inference, and advanced analytical methodologies.
• Partner with operational and technology teams to improve data quality, instrumentation, and measurement capabilities.
• Promote adoption of AI-enabled workflows and modern analytical tools to enhance team productivity and innovation.
• Ensure statistical rigour and best practices through technical reviews, mentorship, and coaching.

Team Leadership & Organisational Impact

• Lead, mentor, and develop a team of analytics and data science professionals.
• Foster a culture of accountability, continuous learning, collaboration, and technical excellence.
• Drive organisational transformation by improving analytical maturity and strengthening cross-functional partnerships.
• Help establish scalable processes, operating models, and frameworks that support business growth and data-driven decision making.

Required Qualifications

• 7+ years of experience in data science, business analytics, revenue analytics, or a related field.
• 3+ years of experience managing and developing analytics or data science teams.
• Strong expertise in B2B SaaS, subscription-based businesses, revenue operations, sales analytics, or marketing analytics.
• Demonstrated success influencing senior executives and business leaders through data-driven recommendations.
• Ability to evaluate and guide advanced analytical approaches, modelling assumptions, and technical implementations.
• Strong proficiency in SQL and the ability to review and assess Python-based analytical solutions.
• Deep knowledge of:
  • Machine learning and predictive modelling
  • Experimental design and A/B testing
  • Statistical analysis
  • Causal inference methodologies
• Excellent communication, stakeholder management, and strategic influencing skills.
• Proven ability to thrive in fast-paced, evolving environments with ambiguity and changing priorities.

Preferred Qualifications

• Experience with modern cloud data andanalytics platforms such as:
  • Snowflake
  • Databricks
  • dbt
  • Airflow
• Experience implementing measurement frameworks and improving data instrumentation.
• Familiarity with AI-assisted analytics, development tools, and workflow optimisation.

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Director, Data Science (Customer Experience)
Remote, US | Base to $300,000 + 20% bonus | Occasional Dallas travel

A large not-for-profit health system is hiring a Director of Data Science to lead an established seven-person AI/ML team.

The mandate is simple to state and hard to do: fix the customer experience outside the hospital room. Healthcare gets the inpatient experience broadly right and the outpatient journey broadly wrong. This team exists to close that gap, and leadership has put real budget and real visibility behind it.

This is not reporting work. It is applied machine learning against genuine customer pain points, at the scale of 55+ hospitals, 1,300+ care sites and 13.5M+ patient encounters a year.

What you’ll own

• The enterprise data science roadmap across customer, clinical and operational priorities
• Predictive, prescriptive and optimisation models: forecasting, segmentation, personalisation, recommendation
• Semantic data products that let chat and AI interfaces query for insight conversationally, rather than returning static output
• The unstructured side of the problem. Survey free-text, centre transcripts and voice-of-customer data hold the highest-value signal here, and turning that into something patients actually feel is the core of the job
• Standards for model validation, deployment, monitoring and lifecycle
• The team itself: structure, coaching, hiring, operating rhythm

What we’re looking for

• 12+ years in data science, machine learning or advanced analytics
• 5+ years directly leading a data science or ML team
• Models in production that changed a business, customer or clinical decision, with numbers attached
• Hands-on with unstructured and text data, not just structured tables
• Strong Python and SQL, modern cloud analytical environments
• Exposure to semantic layers, knowledge products or conversational interfaces over analytical data is a strong plus
• Healthcare experience is welcome but genuinely not required. Retail, banking, travel and consumer tech backgrounds map well, provided you can ramp fast on clinical context

Worth being straight about

This organisation cannot match Big Tech on compensation and does not pretend to. The base tops out at $300,000 with a 20% bonus, and there is no flex above it. What it offers instead is scope, ownership, a green field, and work that matters to three million people.

If comp is the only variable that moves you, this will not be the right seat.

Location

Fully remote within the US, with travel to Dallas roughly once a quarter. Unable to hire from California, New York, Washington, Oregon, Hawaii, North Dakota, Rhode Island or Wyoming.

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Senior MLOps Engineer
London (Hybrid) | £75,000 – £85,000 + Bonus + Benefits
This is an opportunity to take ownership of the infrastructure powering a growing AI platform. You’ll work across machine learning, NLP, and emerging AI technologies, helping build scalable systems that support real-world impact.
The Company
A growing AI and data consultancy helping organisations use data and machine learning to improve decision-making and deliver better outcomes. They are investing heavily in their AI platform and expanding their engineering capability.
The Role

• Own and develop the MLOps infrastructure supporting AI services.
• Build and maintain orchestration pipelines using Dagster, Airflow, or Prefect.
• Deploy and manage ML workloads on Kubernetes and cloud platforms.
• Implement CI/CD, observability, and monitoring across production systems.
• Develop infrastructure using Terraform, Bicep, or similar IaC tools.
• Work closely with Data Scientists, ML Engineers, and AI teams to scale deployments.

Your Skills & Experience

• Strong Python software engineering skills.
• Commercial MLOps experience in production environments.
• Hands-on Kubernetes expertise.
• Experience with workflow orchestration platforms.
• Infrastructure as Code and CI/CD experience.
• Understanding of production ML, NLP, or AI systems.

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Machine Learning Scientist

Overview

A research-driven organisation is seeking a Machine Learning Research Scientist to help develop next-generation foundation models for complex scientific and multimodal datasets. This individual contributor position is ideal for an independent researcher who excels at identifying meaningful problems, designing rigorous experiments, building models, and translating findings into actionable insights.
The role combines advanced machine learning research with practical model development. Success in this position will be measured primarily by scientific contribution, research quality, and the ability to generate new insights through experimentation.

Key Responsibilities

• Design, train, and evaluate large-scale foundation models across diverse multimodal datasets.
• Develop innovative methods for combining information from multiple data sources, modalities, and levels of abstraction.
• Create evaluation frameworks, benchmark tasks, and performance metrics to assess model effectiveness.
• Rapidly prototype research concepts and determine which ideas have the highest potential impact.
• Partner with domain experts, quantitative researchers, and machine learning practitioners to solve complex problems.
• Assess and apply emerging AI approaches, including large language models and autonomous agent-based systems, to research workflows.
• Communicate technical findings to both specialist and non-specialist audiences.
• Contribute to scientific publications, conference submissions, presentations, and broader research engagement activities.
• Lead projects from concept formulation through experimentation, analysis, and final conclusions.

Required Qualifications

Research Excellence

Candidates should demonstrate a strong record of original machine learning research through one or more of the following:

• First-author publications at leading machine learning, artificial intelligence, computer vision, or natural language processing conferences.
• Significant research contributions within highly regarded industrial research environments.
• Doctoral-level research in machine learning, computer science, statistics, artificial intelligence, or another rigorous quantitative discipline.

Key evaluation criteria include:

• Ability to identify important research questions.
• Technical rigor and depth of research execution.
• Evidence of innovative and independent thinking.

Execution Skills

The ideal candidate can:

• Independently move from concept to experiment.
• Implement and train machine learning models.
• Design robust evaluation methodologies.
• Perform comprehensive analysis of experimental outcomes.
• Iterate quickly based on evidence and results.
• Drive complete research efforts rather than contributing only a small portion of a larger initiative.

Academic Background

Strongly preferred:

• PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, Computational Neuroscience, Physics, or another quantitative field.

Candidates without a PhD may be considered if they possess an exceptional research record.

Technical Expertise

High-Priority Areas

Experience in one or more of the following:

• Foundation Models
• Self-Supervised Learning
• Representation Learning
• Computer Vision
• Multimodal Learning
• Large Language Models
• Generative AI
• Diffusion Models
• Flow-Based Generative Modelling
• Autoregressive Models
• Scientific Machine Learning

Additional Relevant Experience

Experience in any of the following areas is beneficial:

• Robotics
• Autonomous Systems
• Computational Biology
• Molecular Modelling
• Protein Structure Prediction
• Molecular Interaction Prediction
• Structural Biology
• Biochemistry
• Quantitative Physics
• Astrophysics
• Other mathematically intensive scientific disciplines

Preferred Qualifications

• Experience applying machine learning to scientific or research-focused domains.
• Familiarity with computational life sciences, chemistry, drug discovery, or related fields.
• Demonstrated interest in using AI to accelerate scientific discovery.
• Strong written and verbal communication skills with the ability to explain complex concepts to diverse audiences.

Domain Knowledge

Prior domain expertise in life sciences is not required. The organisation prioritises machine learning excellence and research capability. Strong candidates may come from areas such as computer vision, language modelling, robotics, reinforcement learning, autonomous systems, or scientific AI.
Candidates should be able to articulate their interest in applying machine learning techniques to scientific challenges and emerging research opportunities.

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Data Scientist
Join a growing Data Science team within an insurance business where you’ll tackle complex pricing challenges, build production-ready machine learning models, and work on projects that directly influence commercial outcomes. This is a great opportunity for someone who enjoys solving business problems as much as building models.

The Role
You will:

• Build, validate and deploy machine learning and statistical models
• Work with Python, SQL, Databricks and PySpark to analyse large datasets
• Solve pricing and risk-related business challenges
• Explore new data sources and test innovative ideas
• Own projects from problem definition through to implementation
• Present insights to technical and non-technical stakeholders
• Support and mentor junior Data Scientists

Your Skills & Experience

• Strong commercial Data Science experience
• Advanced Python and SQL skills
• Experience with Databricks and/or PySpark
• Knowledge of machine learning techniques such as XGBoost and LightGBM
• Strong statistical modelling capability
• Excellent problem-solving and stakeholder management skills
• Experience in insurance, financial services, or another regulated environment is advantageous

To Apply for this Job Click Here

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I’m hiring a Senior Data Scientist to join a leading UK insurer’s Pricing & Data Science function, working on innovative machine learning solutions that directly influence risk assessment, pricing, fraud detection, and commercial performance.

This is a role for someone who enjoys taking models from problem definition through to deployment, working with modern ML techniques and real-world commercial challenges.

1 day/week (Leicester / London / Bexhill)

£60k-£100k

What you’ll be doing

• Build and deploy predictive models across pricing, claims, fraud, and risk analytics.
• Engineer new data assets and rating factors to improve pricing performance.
• Identify and leverage new data sources to drive business value.
• Partner with stakeholders across Pricing, Data Science, Analytics, and Engineering teams.
• Own ML projects end-to-end, including EDA, deployment, monitoring, and model refreshes.

What they’re looking for

– Proven experience delivering ML projects from concept to deployment.
– Strong Python and SQL skills.
– Experience with Azure ML, Git, and cloud-based analytics environments.
– Strong stakeholder engagement and commercial problem-solving capability.
– Passion for applying emerging ML techniques to real business problems.

Why join?

• Work in a highly respected pricing and analytics function.
• Bonus scheme + £5k car allowance.
• Private medical cover and strong benefits package.
• Opportunity to work on innovative modelling and pricing challenges at scale.

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Senior Data Scientist – Leicester or London 1 day a week

We are currently supporting a large insurance company on a Senior Data Science role to sit within their pricing team. This role will challenge core pricing functions and create innovative ways to produce new pricing models using larger and more complex data sources to deliver more competitive pricing and better customer accessibility. It’s a small, fast‑paced team (6 people) working on more experimental and forward‑thinking pricing approaches.

Some of the exciting work includes:

• Alternative pricing models
• Footprint modelling / Geospatial modelling
• Residual modelling to identify gaps between predicted vs. actual claim costs
• Working with large, messy insurance datasets across multiple product lines
• Tools & tech: Python, SQL, AzureML, XGBoost
• Opportunity to mentor within the team

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Director of Quality Engineering

Location: Fully Remote – U.S.
Travel: Occasional travel to Dallas, TX, potentially once per quarter
Compensation: $245,000-$270,000 base + 20% annual bonus
Benefits: Healthcare + 401(k)
Work Authorization: U.S. Citizens and Green Card holders only – no sponsorship

Candidates cannot currently reside in California, New York, Hawaii, North Dakota, Oregon, Rhode Island, Washington, or Wyoming.

About the Company

A large, mission-driven healthcare organization is investing heavily in modern engineering, automation, AI, and customer-facing digital experiences.

Operating at significant enterprise scale, the organization is accelerating its technology strategy and building modern capabilities across software engineering, data, AI, and digital products.

As engineering velocity increases, leadership is rethinking how quality is built into the software development lifecycle. The organization is now establishing a modern, automation-first Quality Engineering function capable of supporting both traditional software and the next generation of AI-enabled products.

About the Role

The Director of Quality Engineering will lead the transformation of an established, predominantly manual QA environment into a modern, automation-first Quality Engineering organization.

This is a true build opportunity.

Today, an existing QA team supports approximately 25 development teams, but there is no dedicated SDET function. The incoming Director will own the existing QA organization while building the technical capabilities, automation frameworks, SDET talent, standards, tooling, and operating model required for the future.

The immediate priority is establishing scalable deterministic automation across API, UI, integration, regression, performance, accessibility, and end-to-end testing while embedding quality directly into CI/CD and engineering workflows.

Longer term, the organization has an ambitious vision for AI-enabled and agentic testing-moving toward intelligent systems capable of generating tests, identifying defects, selecting regression coverage, and eventually executing increasingly complex testing workflows based on intent.

The successful candidate will be a builder who has transformed Quality Engineering before and is excited by the opportunity to create the strategy rather than inherit one.

Key Responsibilities

• Define and execute the enterprise Quality Engineering strategy
• Lead the transformation from manual-heavy QA toward an automation-first engineering model
• Build a multi-year roadmap across automation, tooling, talent, metrics, and operating model modernization
• Establish and scale a dedicated SDET and test automation capability
• Lead the existing QA organization through its transition toward a more technical, automation-oriented model
• Design scalable automation frameworks across API, UI, integration, regression, performance, accessibility, and end-to-end testing
• Embed automated testing into CI/CD pipelines, development workflows, and release gates
• Establish shift-left testing practices and reduce reliance on late-stage manual validation
• Define enterprise standards for quality gates, release readiness, regression testing, defect triage, and production validation
• Establish meaningful metrics around automation coverage, defect leakage, test effectiveness, release predictability, and production stability
• Introduce AI-enabled approaches to test generation, maintenance, defect analysis, regression selection, and synthetic data
• Partner with Engineering, Product, Architecture, Security, Data, AI, and Operations teams
• Establish evaluation and quality practices for AI-enabled and agentic systems
• Support validation of AI behavior, guardrails, traceability, safety, errors, fallback behavior, and human-in-the-loop workflows
• Build, coach, and develop technical Quality Engineering talent

Must Haves

• 15+ years of experience across Quality Engineering, software quality, test automation, Software Engineering, or related technology delivery
• 7+ years of Quality Engineering, SDET, automation, QA, or engineering leadership
• Proven experience transforming manual QA environments into automation-first Quality Engineering organizations
• Experience building or substantially scaling test automation capabilities
• Experience leading technical Quality Engineering, SDET, or engineering-adjacent teams
• Deep understanding of modern test automation architecture and frameworks
• Strong experience across API, UI, integration, regression, performance, and end-to-end testing
• Experience embedding automated testing into CI/CD pipelines and release gates
• Strong understanding of shift-left testing and engineering-owned quality
• Experience defining quality gates, release-readiness standards, automation metrics, and testing strategy
• Strong understanding of modern software engineering, Agile, CI/CD, and DevSecOps
• Experience leading significant technical and organizational transformation
• Ability to influence Engineering, Product, Architecture, Security, Operations, and executive stakeholders
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or another related technical discipline

AI & Next-Generation Quality Engineering

The strongest candidates will bring a point of view on where Quality Engineering is heading-not simply how testing has traditionally been done.

Experience or strong technical exposure in areas such as the following will stand out:

• AI-assisted test generation and maintenance
• Intelligent regression selection
• AI-assisted defect analysis
• Synthetic test data
• AI evaluation frameworks
• GenAI and conversational-system testing
• Expected-behavior and guardrail validation
• Hallucination and error detection
• Human-in-the-loop validation
• Agentic or intent-based testing
• Automated monitoring and feedback loops for AI systems

The longer-term objective is not simply to automate manual test cases. It is to create a Quality Engineering capability capable of supporting increasingly intelligent and autonomous software systems.

Nice to Have

• Playwright, Maestro, or comparable modern automation frameworks
• Experience building an SDET organization from the ground up
• Experience transforming an established manual QA workforce
• AI, GenAI, machine learning, or agentic-system testing experience
• Experience with customer-facing or mission-critical digital products
• Healthcare or health technology experience
• Financial services, life sciences, or another regulated/high-trust industry
• Experience managing vendor, partner, or distributed QA resources
• Master’s degree in a relevant technical discipline

Healthcare experience is valuable but is not the primary requirement for this search. Deep automation expertise and experience building technical Quality Engineering capabilities are significantly more important.

Why Join

• Build an enterprise Quality Engineering capability from a near-blank slate
• Define the automation strategy, frameworks, tooling, standards, and operating model
• Establish a dedicated SDET capability
• Transform an established manual QA environment rather than simply optimize an already mature automation program
• Own both the technical strategy and organizational evolution
• Build automation directly into modern engineering and CI/CD workflows
• Help define how Quality Engineering evolves for GenAI and agentic systems
• Work toward an ambitious longer-term vision for intent-based and agentic testing
• Partner directly with senior Engineering, Product, Architecture, Data, and AI leaders
• Work on customer-facing technology where quality and reliability have meaningful real-world impact
• Fully remote with limited travel to Dallas, TX

To Apply for this Job Click Here

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Overview

A large, complex enterprise is seeking a senior data science leader to build and scale its data science function. This role is responsible for translating data into predictive, prescriptive, and AI-enabled insights that improve customer experiences, operational performance, decision-making, and organisational outcomes.
The Director, Data Science will lead teams focused on statistical modelling, machine learning, experimentation, forecasting, optimisation, decision support, and AI-enabled analytics. The position partners closely with data, engineering, product, digital, and business stakeholders to ensure analytical solutions are built on trusted data and deliver measurable value.
This leader will establish best practices for model development, validation, deployment readiness, monitoring, and lifecycle management while fostering a high-performance, innovation-focused culture.

Key Responsibilities

Data Science Strategy & Leadership

• Define and execute the enterprise data science roadmap aligned to strategic business priorities.
• Identify opportunities where advanced analytics, machine learning, optimisation, and AI can improve organisational outcomes.
• Prioritise investments based on business value, feasibility, data readiness, and strategic importance.
• Establish standards for experimentation, model development, validation, documentation, monitoring, and governance.
• Ensure solutions are scalable, reusable, interpretable where appropriate, and aligned with enterprise architecture and responsible AI principles.

Advanced Analytics, Machine Learning & AI

• Lead development of predictive, prescriptive, optimisation, and forecasting models across multiple business domains.
• Oversee customer segmentation, risk modelling, recommendations, personalisation, propensity modelling, and decision-support solutions.
• Partner with engineering and product teams to operationalise analytical capabilities within production environments and business workflows.
• Support AI initiatives by defining methodologies, evaluation approaches, and performance expectations.
• Promote modern statistical and machine learning techniques while ensuring appropriate application and risk management.

Model Evaluation, Experimentation & Measurement

• Define frameworks to evaluate model performance, business impact, reliability, fairness, and adoption.
• Lead experimentation strategies including A/B testing, causal inference, impact measurement, and test design.
• Collaborate with engineering, product, quality, and business teams on model monitoring and continuous improvement.
• Translate analytical outputs into actionable insights and business recommendations.
• Establish reporting, dashboards, and executive-level performance reviews.

Cross-Functional Collaboration

• Partner with enterprise data teams to ensure access to reliable, high-quality data.
• Collaborate with knowledge management, semantic modelling, and data product teams to improve analytical outcomes.
• Work closely with product, operations, technology, and business leaders to identify and prioritise analytical opportunities.
• Support production deployment of models and analytical services in partnership with engineering teams.
• Provide strategic guidance on where advanced analytics and AI can create value, and where alternative approaches may be more appropriate.

Team Leadership & Development

• Build, lead, and mentor a high-performing team of data scientists and analytics professionals.
• Define organisational structure, hiring strategy, capability development, and career progression pathways.
• Establish effective collaboration models across data, engineering, governance, product, and business teams.
• Coach team members on technical excellence, stakeholder engagement, communication, and business impact.
• Foster a culture of innovation, accountability, continuous learning, and responsible AI.

Required Qualifications

Education

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, Operations Research, Public Health, Informatics, or a related field.
• Master’s degree or PhD preferred.

Experience

• 12-18+ years of experience in data science, machine learning, advanced analytics, applied statistics, or related disciplines.
• 5-8+ years leading data science, analytics, or machine learning teams.
• Proven experience delivering analytical solutions that influence business or operational decision-making.
• Experience partnering with engineering, technology, product, and executive stakeholders.
• Strong background in model development, experimentation, monitoring, and production deployment.
• Experience within a regulated industry preferred.

Technical Expertise

• Deep expertise in statistical modelling, machine learning, predictive analytics, optimisation, experimentation, and decision science.
• Proficiency with Python, R, SQL, notebook-based development environments, and modern machine learning frameworks.
• Experience assessing data quality, feature readiness, model performance, bias, drift, interpretability, and operational fit.
• Understanding of model deployment, monitoring, integration, and lifecycle management within production environments.
• Familiarity with AI, Generative AI, large language model evaluation, recommendation systems, forecasting, personalisation, and decision-support methodologies.
• Strong knowledge of privacy, security, governance, and responsible AI practices.

Preferred Qualifications

• Experience leading data science teams within healthcare, life sciences, financial services, or other highly regulated industries.
• Experience supporting AI-enabled products, digital experiences, operational optimisation, workflow automation, or decision-support capabilities.
• Experience building reusable analytical frameworks and platforms that support multiple business functions.
• Experience defining enterprise standards for model evaluation, observability, monitoring, and continuous improvement.
• Familiarity with semantic models, ontologies, knowledge graphs, or enterprise knowledge management frameworks.
• Strong executive communication and stakeholder management skills.

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