Associate Director, Data Products, Operational Data Strategy

Introduction to the role

The Operational Data Strategy team are looking for an exceptional candidate to support a growing portfolio of data assets. Reporting to the Director, Data Products, the Associate Director, Data Products will set the vision and drive the execution of a suite of data products that will be utilised across R&D. The role translates business problems into compelling product capabilities, orchestrates multidisciplinary delivery teams, and ensures every release delivers measurable value to the business.

The successful candidate will own end-to-end product lifecycle management for assigned data products. They shape product strategy in partnership with R&D IT and functional stakeholders; create and maintain outcome-driven roadmaps; manage product backlogs and technical debt; govern data quality, privacy, and FAIR principles; and champion user adoption and continuous iteration. Acting as the single point of accountability, they balance short-term deliverables with long-term platform scalability while navigating a regulated, global environment.

Typical Accountabilities

• Engage business leaders to surface high-value use-cases for data products under your remit; convert those needs into clear product charters, north-star metrics, and release plans.
• Partner with architecture and engineering leads to design solution blueprints that blend structured data assets, unstructured document repositories, and advanced AI services and related tooling.
• Drive agile ceremonies, backlog prioritisation, and sprint planning, ensuring every user story is testable, regulatory compliant, and traceable to business value.
• Protect and enhance data quality by embedding robust stewardship practices and metadata management across structured clinical data and document-derived entities.
• Define product KPIs (e.g., query success rate, time-to-insight, document-extraction precision) and manage benefit realisation against agreed targets.
• Lead change-management, communication, and training efforts so that clinicians, trial managers, and data scientists rapidly adopt new features and workflows.
• Identify and mitigate risks spanning technical feasibility, ethics, regulatory constraints (ICH E6(R3), GDPR), and model-governance requirements.

Education, Qualifications, Skills and Experience

Essential

• Candidates must demonstrate proven success guiding data-heavy products from concept to value realisation, ideally within life-sciences R&D.
• Fluent in product-management principles, agile and DevOps ways-of-working; skilled at gathering and documenting technical requirements, data mapping, user stories, and process flows.
• Strong technical background and familiarity with foundational data-engineering topics such as data modelling, data lakes, and APIs.
• Ability to explain transformer models, vector search, prompt engineering, and retrieval-augmented generation at a level sufficient to align with architects and data scientists.
• A bachelor’s degree in a scientific, technical, or quantitative discipline is required.
• Proficiency in English (spoken and written).
• Knowledge of SQL, data querying, and data manipulation.

Desirable

• Familiarity with enterprise data platforms (Starburst, Snowflake).
• Familiarity with data visualization tools such as Tableau, Power BI, or similar.
• Understanding of operational data management principles and methodologies.
• Knowledge of data governance, data quality management, and regulatory requirements for healthcare data.
• Experience with clinical-trial systems (e.g., CTMS, eTMF, CDMS), knowledge of Snowflake ecosystem services (Cortex AI, Snowpipe, Streamlit in Snowflake), and a track record of delivering analytics products will be advantageous.
• A master’s or PhD, GxP familiarity, and agile certifications (e.g. SAFe) are desirable differentiators.