Finance Portfolio Analytics Analyst

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 Analyst is the hands-on engine behind the analysis that guides R&D portfolio and investment decisions. You will build and maintain the financial and operational models, forecasts and dashboards the team relies on, and support ad hoc strategic analyses — pulling together data from scientific, clinical, financial and operational sources and turning it into clear, accurate outputs.

Working closely with the Head and alongside the Finance Portfolio Analytics Manager, this role is an opportunity to develop deep portfolio-analytics expertise in a high-visibility environment and to grow toward independent ownership of analyses.

Key Responsibilities:

Analysis & Modelling Build, populate and maintain financial and valuation models (e.g. NPV/rNPV, scenario and sensitivity analysis) under guidance.

Support ad hoc strategic and exploratory analyses; gather, clean and structure data from scientific, clinical, financial and operational sources.

Help develop and run probabilistic and Monte Carlo simulations and other quantitative analyses.

Portfolio reporting & forecasting

Maintain portfolio forecasts, metrics, milestones and KPI tracking, and flag variances against plan.

Build and maintain dashboards, reports and decision materials for review discussions.

Support improvements to data quality, reporting tools and forecasting methods.

Decision support & collaboration

Prepare clear, accurate materials for portfolio and product review discussions.

Synthesise quantitative and qualitative findings into concise summaries, and communicate results clearly to non-technical stakeholders.

Partner across development, operations, finance and technology teams, taking direction from the Head and senior analytics colleagues and following through on commitments.

Basic Qualifications:

Bachelor’s degree in a quantitative field (engineering, computer science, mathematics, economics, operations research or finance) or life sciences with specific quantitative experience; or equivalent professional experience.
2+ years’ experience in financial and operational modelling, data analysis, analytics or forecasting;2+ years’ experience turning data and analysis into clear, actionable insights and recommendations.
2+ years’ experience coding in at least one analytical or programming language (e.g. Python, R or SQL), with exposure to data science app development frameworks (e.g. Shiny, Streamlit or Dash).

Preferred Qualifications:

Experience in pharmaceuticals, biotech, life sciences, consulting or financial services.

Exposure to drug development or R&D portfolio concepts. Strong quantitative fundamentals, with accuracy and attention to detail.

Ability to communicate analytical results clearly to non-technical stakeholders and to work effectively in cross-functional teams.

Strong data science fundamentals, with exposure to valuation methods (NPV/rNPV), scenario and sensitivity analysis and Monte Carlo simulation.

Experience with web app development, from either a data engineering or UI perspective (e.g. Shiny, Streamlit or Dash).Interest in or experience with Generative AI tools and frameworks.

Familiarity with standard software development tools and practices (e.g. Git, CI/CD, containerisation).Comfort with ambiguity…