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Head of AI Research

$300k-$350k

Overview

A mission-driven research organisation is seeking a Chief Scientist to lead its scientific strategy, research operations, and interdisciplinary research teams. Reporting directly to the Chief Executive Officer and serving as a member of the executive leadership team, this individual will oversee multiple research functions and ensure alignment between scientific objectives, organisational priorities, and long-term impact.
This role focuses on building and leading a high-performing research organisation rather than serving as an individual contributor. The successful candidate will bring deep expertise in artificial intelligence, substantial leadership experience, and the ability to integrate technical and scientific disciplines into a cohesive research strategy.

Key Responsibilities

Scientific Leadership & Strategy

• Define and oversee a multi-year scientific roadmap aligned with organisational mission and objectives.
• Ensure close collaboration between AI-focused research and domain science teams to drive meaningful scientific outcomes.
• Maintain high standards of scientific rigor, reproducibility, and research integrity.
• Monitor relevant developments in machine learning, computational science, behavioural research, and related disciplines, translating insights into strategic opportunities.

Research Organisation Leadership

• Lead, mentor, and develop senior research leaders and multidisciplinary teams.
• Design organisational structures, staffing plans, and growth strategies to support research priorities.
• Recruit and retain exceptional scientific and technical talent, particularly for senior leadership positions.
• Partner with programme and operational leaders to prioritise initiatives, allocate resources, and ensure effective execution.
• Foster an inclusive, collaborative, and high-performing research culture.

Executive Leadership

• Serve as a strategic partner to executive leadership on organisational direction and long-term planning.
• Align research priorities with fundraising, communications, and organisational initiatives.
• Present research strategy, progress, and key decisions to leadership, funders, boards, and external stakeholders.
• Support external communications by ensuring scientific findings and organisational priorities are accurately represented.

Financial Management

• Own research budget planning and spending within approved parameters.
• Develop and manage multi-year research budget forecasts in partnership with organisational leadership.
• Monitor expenditures and proactively identify significant budgetary changes or risks.

External Engagement

• Represent the organisation at scientific, industry, and professional events.
• Develop partnerships with academic institutions, government organisations, nonprofits, and research collaborators.
• Contribute to publications, thought leadership, and public-facing scientific communications.
• Expand external research networks through fellowships, internships, and academic outreach programmes.

Culture & Ethical Leadership

• Promote cross-functional collaboration and transparent knowledge sharing.
• Create an environment that values respect, inclusion, and diverse perspectives.
• Encourage participation from researchers at all levels in scientific planning and decision-making.
• Uphold strong ethical standards in research design, data usage, interpretation, and communication.

Required Qualifications

Education

• PhD in Machine Learning, Computer Science, Electrical Engineering, Computational Biology, or a closely related discipline.
• Strong scientific credibility and research accomplishments, though an active publication record is not required.

Experience

• 15+ years of experience spanning AI research, large-scale AI implementation, or a combination of both.
• Demonstrated success leading research organisations, technical teams, or advanced AI programmes.
• Significant experience evaluating research quality and managing senior researchers or technical leaders.
• Experience building teams, setting strategic direction, and maintaining scientific standards.
• Strong professional network within the AI research and technology community.
• Ability to recruit and develop senior-level scientific talent.

Technical Expertise

• Deep understanding of modern AI and machine learning methodologies.
• Familiarity with foundation models, large-scale model training, and production-scale AI systems.
• Experience assessing research quality across interdisciplinary environments.
• Understanding of quantitative scientific research methods and data-driven experimentation.

Leadership Skills

• Proven ability to lead complex, multidisciplinary research organisations.
• Strong organisational judgement and strategic decision-making skills.
• Excellent communication skills with executive, scientific, funding, and public audiences.
• Ability to bridge different research cultures and disciplines effectively.
• Passion for mission-oriented scientific research and long-term impact.

Preferred Qualifications

• Experience working in both academic and commercial research environments.
• Background applying AI to scientific or natural-world research domains.
• Familiarity with machine learning for audio, speech, computer vision, or related data modalities.
• Experience in nonprofit, research institute, mission-driven, or field-based scientific organisations.
• Knowledge of responsible AI frameworks and ethical research practices.

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Senior Data Scientist

London (Hybrid – 1 Day Per Week)

£60,000-£75,000 + £5,000 Car Allowance + 10% Bonus + Outstanding Benefits

THE COMPANY

We’re exclusively partnering with one of the UK’s leading digital insurance businesses to hire two Senior Data Scientists into its highly regarded Alternative Pricing Product (APP) team.

With over four million customers and significant investment in Data Science, Machine Learning and digital transformation, the business has built one of the strongest pricing and analytics capabilities in UK insurance. This is a genuinely innovative environment where Data Scientists are encouraged to challenge conventional pricing methods, explore new data sources and develop cutting-edge machine learning solutions that directly influence commercial decisions.

THE ROLE

Joining a specialist team of six Data Scientists and Analysts, you’ll play a key role in developing the next generation of pricing models across car and van insurance.

Rather than maintaining existing models, you’ll be encouraged to challenge traditional pricing approaches by exploring new datasets and applying innovative machine learning techniques to solve complex commercial problems.

Responsibilities include:

• Building predictive machine learning models using large, complex datasets
• Developing innovative pricing and customer selection models
• Creating footprint (geospatial) models using alternative data sources
• Developing residual models to improve claims prediction accuracy
• Exploring novel datasets to improve pricing performance
• Taking existing V1 models and evolving them into production-ready V2 solutions
• Working closely with Pricing, Data Science and Engineering teams
• Mentoring a Graduate Data Scientist while leading your own projects from concept through to delivery

YOUR SKILLS AND EXPERIENCE

• Strong Python and SQL experience
• Commercial Machine Learning experience
• Strong predictive modelling background
• Comfortable working with large, messy datasets
• Experience solving complex business problems using data
• Experience mentoring or supporting junior team members
• Exposure to model deployment is beneficial but not essential
• Experience within Insurance, Financial Services, Gaming or another digital-first business is advantageous

Experience with Azure Machine Learning, Graph-based models or geospatial modelling would be beneficial but is not essential.

THE BENEFITS

• Salary up to £75,000 (up to £80,000 for exceptional candidates)
• £5,000 annual car allowance
• 10% target bonus (up to 20%)
• Hybrid working (1 day per week in the office)
• Choice of London or Leicester or Bexhill office, with monthly London travel fully expensed
• Private Medical Insurance
• 10% matched pension
• 27 days annual leave plus bank holidays
• Buy or sell up to 5 days annual leave
• Life Assurance and Income Protection
• Health & Wellbeing package
• Optional dental cover
• Discounts on company products

INTERVIEW PROCESS

• 30-minute introductory conversation with the Hiring Manager
• Take-home case study followed by a 20-minute presentation and technical discussion
• Final leadership and team-fit interview

HOW TO APPLY

Please register your interest by sending your CV to Max Beadle at Harnham via the Apply link on this page.

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Lead Data Scientist

Location: Hybrid (Tempe, AZ preferred; Dallas, TX or Atlanta, GA also considered)

Schedule: 4 days onsite per week (Monday-Thursday)

Compensation: $130-145,000 base salary + bonus eligibility

Overview

A leading organization is seeking a Lead Data Scientist to drive advanced risk and financial modeling initiatives that directly influence business performance. This is a highly visible individual contributor role for a senior-level data scientist who can independently lead complex projects, quantify business impact, and effectively communicate insights to executive stakeholders.

This team is responsible for identifying significant financial opportunities, developing predictive models, and delivering measurable outcomes. Success in this role requires strong technical expertise, business acumen, and the ability to defend models in regulated environments.

Responsibilities

• Lead end-to-end data science projects from problem definition through implementation.
• Develop, validate, and monitor predictive and risk models using large-scale financial and customer datasets.
• Apply advanced statistical and machine learning techniques, including gradient boosting models.
• Translate model outputs into actionable business recommendations and financial impact.
• Present findings, model performance, and recommendations to senior leadership and executive stakeholders.
• Support model governance activities, audits, and validation reviews.
• Mentor and support team members while serving as a technical leader within the organization.

Required Qualifications

• 8+ years of experience in data science, quantitative analytics, or predictive modeling.
• Strong background in finance, credit, or risk modeling within a regulated industry.
• Advanced Python and SQL skills.
• Strong foundation in mathematics, statistics, and predictive modeling.
• Experience building and validating models used for financial or risk-related decision making.
• Experience presenting technical concepts and model results to executive audiences.
• Proven ability to independently own and deliver complex projects.
• Experience supporting external audits and defending analytical models.

Preferred Experience

Experience in one or more of the following areas:

• Credit risk modeling
• Probability of Default (PD) modeling
• Credit loss or loss forecasting models
• Scorecard development
• Payment behavior analytics
• Banking, lending, mortgage, credit card, or credit bureau data
• Financial impact and portfolio analysis

Education

• Bachelor’s degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or another quantitative discipline required.
• Master’s degree in Data Science, Statistics, Mathematics, Economics, Engineering, or a related quantitative field preferred.

Why Join?

• Direct ownership of high-value analytics initiatives.
• Opportunity to see models implemented and tied to measurable business outcomes.
• Significant executive exposure and influence.
• High-impact, collaborative team where individual contributions are highly visible.
• Ability to drive projects that identify and deliver multi-million-dollar business opportunities.

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Lead Data Scientist

We are looking for a Lead Data Scientist to own complex quantitative and risk modeling projects from problem definition through implementation. This is a highly visible role where your models will directly influence multi-million-dollar business opportunities and be presented to executive stakeholders.

What You’ll Do

• Build, evaluate, and validate quantitative and risk models.
• Work with large financial and credit datasets.
• Develop models using Python, SQL, and gradient boosting techniques.
• Translate model outputs into measurable financial and dollar impact.
• Own projects end-to-end, from defining the problem through implementation.
• Present technical findings and recommendations to senior and executive stakeholders.
• Defend models and methodology during external audits.
• Help identify and quantify significant financial opportunities.
• Provide technical guidance and support to other team members.

What We’re Looking For

• 8+ years of data science/modeling experience
• Strong Python and SQL skills.
• Strong data modeling experience.
• Strong mathematical and statistical foundation.
• Experience with gradient boosting models.
• Proven ability to independently lead projects end-to-end.
• Excellent communication and presentation skills.
• Comfortable explaining technical work and model results to executives.
• Experience working through external audits and defending models.
• Finance/risk modeling experience within a regulated industry is required.

Required Domain Experience

You should have experience in areas such as:

• Credit risk modeling
• Financial or loss modeling
• Probability of Default (PD) modeling
• Credit scorecards
• Payment behavior modeling
• Credit attribute modeling
• Mortgage, credit card, banking, or credit bureau data
• Risk modeling within a regulated financial institution

Education

• Bachelor’s degree in Mathematics, Statistics, Economics, Computer Science, Quantitative Engineering, or another highly quantitative field.
• Master’s degree in Data Science or a related quantitative discipline preferred.

The Ideal Candidate

This role is suited to a senior-level Data Scientist who can operate independently, drive complex projects, and communicate confidently with both technical and executive audiences.

Location: Tempe, AZ preferred; Dallas or Atlanta also considered
Working Model: Hybrid, 4 days per week (Monday-Thursday)
Compensation: $140,000-$150,000 base
Visa: U.S. Citizen or Green Card holder only

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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

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Staff Data Scientist (Product)

Location: Remote

Salary: $190-220k

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role embedded in their product organization. This is for someone who combines deep statistical expertise with the ability to influence how an entire company makes decisions – not just someone who runs experiments, but someone who builds the culture and infrastructure around them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focused on high-leverage product questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to measure the real impact of product changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to meaningful business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of Product Data, acting as a strategic thought partner across Product, Engineering, Finance, Design, and Product Marketing. The company is at an inflection point in how it uses data to make product decisions, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still developing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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Staff GTM Data Scientist

Location: Remote

Salary: $190-220k base

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role sitting at the heart of their product and go-to-market strategy. This isn’t a reporting role or a dashboard-builder position. It’s for someone who can drive a genuine shift toward data-driven decision making across the organization, and who has the technical depth and communication skills to bring leadership along with them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focusing on high-leverage questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to assess the real impact of product and business changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of GTM Data, acting as a strategic thought partner across Product, Marketing, Finance, and Engineering. The company is at an inflection point in how it uses data, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still maturing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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