Portfolio Data Science Director

GSK•Upper Providence, PA

9d•Hybrid

About The Position

The Head, Data Science, Analytics, and AI sets the vision for, builds and leads the data science, analytics and AI capability that underpins R&D portfolio and strategic resource-management decisions. A peer to the Senior Director, Portfolio Projects, you will own the strategy, talent, standards and delivery of a multidisciplinary team —Portfolio, Finance and Strategic Resource Management teams — and be accountable for turning data into decision-ready analytics applications that internal business users rely on every day. This is a senior people-leadership role. You will lead, grow and develop a team of data scientists, analysts and engineers; set technical direction across data science and analytics projects; and partner with senior leaders across development operations, research units, finance and technology to translate business need into high-impact, responsibly built solutions.

Requirements

Doctoral degree in a quantitative or scientific discipline (e.g. statistics, computer science, engineering, operations research, economics, physical or life sciences) with 10+ years of relevant experience; or a Master’s degree with extensive years of relevant experience.
Demonstrated people-leadership experience, leading and developing teams of data scientists and technical/analytics project teams.
Demonstrated ability to deliver analytics applications to internal business users, from concept through production adoption.
Deep expertise across data science, analytics and AI methods, with the judgement to match methods to business decisions.
Experience partnering with and influencing senior and executive stakeholders through structured, data-backed recommendations.
Experience managing prioritisation and delivery across a portfolio of technical projects to agreed timelines.

Nice To Haves

Experience in pharmaceuticals, biotech or another R&D-intensive industry, ideally with exposure to portfolio, investment or resource-management decision-making.
Track record of building or scaling a data science / analytics function, including platforms, standards and ways of working.
Hands-on grounding in modern data science and software delivery (e.g. Python/R, cloud, app development frameworks, CI/CD, containerisation).
Experience establishing responsible-AI, model-governance and data-quality practices.
Experience with Generative AI tools and frameworks and their responsible application to business problems.
Experience working in a matrixed, global organisation.

Responsibilities

Define and own the strategy for data science, analytics and AI in support of R&D portfolio investment and strategic resource-management decisions.
Shape the roadmap of models, analytics applications and capabilities, balancing near-term delivery with long-term capability build.
Act as a peer to the Senior Director, Portfolio Projects, aligning the analytics agenda with portfolio, finance and operational priorities.
Lead, hire, grow and develop a multidisciplinary team of data scientists, analysts and engineers, to support analytics across multiple teams including the Finance, Portfolio and Strategic Resource Management teams.
Set technical direction and standards across data science and analytics projects; manage prioritisation, delivery and quality across a portfolio of work.
Provide coaching, career development and performance management, building a high-performing, inclusive team culture.
Be accountable for delivering robust, usable analytics applications and products to internal business users — from valuation and forecasting tools to resource-planning and KPI dashboards.
Ensure solutions are production-grade, well-supported and adopted, with clear ownership, documentation and user enablement.
Drive reusable platforms, patterns and standards that scale analytics delivery across the function.
Partner with and influence senior and executive stakeholders, presenting analysis, trade-offs and recommendations that shape investment and resourcing decisions.
Own analytical governance, data quality and methodological rigour across the function’s deliverables.
Champion the responsible, compliant use of Gen AI and advanced analytics in a regulated environment.