Head of Data and Data Science, R&D, ED&AI

Description

The company is a global biopharma business aiming for £40 billion in annual sales by 2031, with a focus on early intervention to prevent and change the course of disease. Its portfolio spans vaccines, specialty medicines and general medicines.

The company keeps developing new treatments for major health challenges through steady innovation and a strong focus on science and technical quality.

This is a senior leadership role for a seasoned data and data science leader who wants to shape how trusted data, advanced analytics and agentic AI speed up R&D decisions.

You will be Director of Data & Data Science, Development Data, in the Enterprise Data & AI organisation. You will own the strategy, organisation and delivery of data and data science across R&D, including a set of R&D Data Categories led by Senior Technical Data Product Owners.

You will set portfolio direction, lead people and investment decisions, set consistent governance and delivery standards, and represent the function to senior R&D and Enterprise Data & AI leaders.

You will stay close to the technology and science, spending at least 50% of your time hands-on: writing code, building and evaluating models, and developing agentic AI systems that do data science work under appropriate human oversight.

This is a visible role to build a high-impact function where data, science and AI meet.

Responsibilities

• Define a multi-year R&D data and data science vision and turn it into a focused roadmap covering all R&D Data Categories.
• Make sure each category has clear ownership, a sound roadmap and measurable scientific or business value.
• Allocate engineering, data science, platform capacity, technical debt, governance and innovation work across the portfolio.
• Build and grow the TDPO organisation: set clear expectations, settle trade-offs between categories, and lead hiring, organisational design and career development.
• Embed shared engineering and data science standards, strong review practices and active communities of practice.
• Ensure consistent data quality, metadata, access controls, privacy and audit readiness across R&D data.
• Work with senior scientific leaders to find, sponsor and unlock the highest-value data science and AI opportunities.
• Personally build agentic AI systems that explore data, test hypotheses, run analyses and produce trusted insights under appropriate human oversight.
• Shape architecture and tooling, build strong investment cases, manage budgets and oversee key supplier relationships.
• Speak for the function to senior R&D and Enterprise Data & AI leaders, reporting on portfolio health, adoption, risk, capacity and value delivered.

Requirements

• Experience leading a data, analytics or data science organisation in a regulated environment, owning strategy, budget and several direct reports.
• Experience leading leaders, such as product owners, engineering managers or senior individual contributors who own significant scope.
• A record of setting and delivering a multi-year data and data science strategy that won senior stakeholder backing and funding.
• Technical depth in data engineering and data science, good enough to judge architecture, priorities, hiring and escalations.
• Ability and willingness to spend at least 50% of working time hands-on: writing code, analysing data, building and evaluating models and developing agentic AI systems.
• Experience building or deploying agentic AI systems that do data science work, including exploration, hypothesis generation, analysis and insight generation.
• Working knowledge of R&D data governance, including blinded and unblinded access, GxP, data privacy and audit readiness.
• Strong executive communication and stakeholder management, with the ability to make portfolio-level trade-offs across competing priorities.
• Nice to have: prior work as a Technical Data Product Owner, Data Product Owner or an equivalent domain or category-owning role.
• Nice to have: familiarity with clinical and life-sciences data standards such as CDISC SDTM/ADaM, OMOP CDM or IDMP.
• Nice to have: experience running a formal data-fabric or data-mesh model with domain-oriented ownership and marketplace-style data product discovery at enterprise scale.
• Nice to have: experience building or scaling a data science or AI/ML function, an advanced quantitative or life-sciences degree, or experience with causal machine learning in R&D, clinical or real-world data.

Conditions

Applications close on 13 November 2026.

The company works to an agile model for all roles, so ask the hiring team what flexibility is possible.

Candidates needing adjustments in the process can write to the company's site, where they can also request a call. Other enquiries cannot be handled through that address, but a Recruitment FAQ guide is available.