Finance Portfolio Analytics Manager

GSK•Upper Providence, PA

7h•Hybrid

About The Position

At GSK we unite science, technology and talent to get ahead of disease together. Deciding which R&D projects to invest in — and how to sequence, resource and prioritise them — is one of the most consequential judgements we make. The Portfolio Data Science team turns scientific, clinical, operational and financial inputs into the analysis and insight that guide those investment decisions across short-, medium- and long-range horizons. Position Summary Reporting to the Head, Data Science, Analytics, and AI, the Finance Portfolio Analytics Manager builds and leads the analytical capability that underpins R&D portfolio and investment decision-making. You will design the models, frameworks and dashboards the team relies on, and lead ad hoc strategic analyses that answer complex, high-stakes portfolio questions — translating scientific and clinical information into quantified investment and financial impact. This is a senior individual-contributor role. You will not have direct reports, but you will act as the analytical lead for portfolio analytics — setting standards, guiding and mentoring analysts, and being the trusted partner senior stakeholders turn to for rigorous, decision-ready analysis.

Requirements

Bachelor’s degree in a quantitative field (engineering, computer science, mathematics, economics, operations research, finance ) or life sciences with specific quantitative experience.
Demonstrated experience in financial & operational modelling, valuation, investment analysis, decision science or a related area field.
Demonstrated expertise translating data science insights into actionable recommendations.
Experience defining problem statements from limited initial information and managing multiple parallel priorities to agreed timelines.
Experience preparing and presenting executive-level analysis and influencing decisions through structured communication and data-backed recommendations.
Proficiency with least one analytical or programming language (e.g. Python or R), together with exposure to data science app development frameworks (Shiny, Streamlit, Dash etc.)

Nice To Haves

Advanced degree (MBA, MSc or equivalent) in a finance, quantitative, scientific or business discipline.
Direct exposure to drug development and R&D portfolio or investment decision-making in pharmaceutical, biotech or a related industry.
Strong data science fundamentals with particular exposure to Monte Carlo modelling
Experience with web app development either from a data engineering or UI perspective
Experience with Generative AI tools and frameworks
Experience with standard Software Development tools and practices (Git, CI/CD, Containerization etc. )

Responsibilities

Design, build and own the portfolio analytics toolkit — valuation models (NPV/rNPV), scenario and sensitivity analysis, probabilistic and Monte Carlo simulation, and portfolio optimisation and prioritisation approaches.
Establish reusable, well-documented models, templates and standards so that analyses are transparent, reproducible and auditable.
Continuously improve forecasting methodologies, data quality, and reporting and analytics tooling.
Lead ad hoc strategic analyses to address complex, ambiguous investment and portfolio questions, defining the problem statement where only limited framing exists.
Develop, evaluate and stress-test business cases, valuations and investment scenarios.
Translate scientific, clinical and operational inputs into quantified financial and investment impact.
Build and maintain portfolio forecasts across annual, multi-year and long-range planning horizons.
Define and track portfolio metrics, milestones and KPIs; monitor performance against plan and surface variances and risks with recommended actions.
Support investment allocation and budget phasing aligned to portfolio priorities.
Prepare insights, materials and recommendations for portfolio and product review discussions and investment governance cycles.
Present trade-offs, risks and financial impact to senior leadership, and influence decisions through structured, data-backed recommendations.
Consolidate inputs from scientific, clinical, regulatory, commercial, finance and technology stakeholders into a coherent portfolio view.
Partner across development, operations, finance and technology teams to align on assumptions, data and methods.
Set analytical standards and mentor and guide analysts; champion best practice and the responsible use of Gen AI and advanced analytics to accelerate and strengthen analysis.