Commercial Forecasting & Analytics Lead
gsk
Job description
About the role
GSK is looking for a leader of its Commercial Forecasting & Analytics department in Japan. The role leads a team of forecasting, integrated demand planning and analytics professionals, shapes forecasting strategy and gives executives robust forecasts, market insights and scenario-based recommendations. It works closely with Commercial, Finance, Medical and Market Access, and builds team capability and a data-driven culture.
Key responsibilities
- Lead Japan's commercial forecasting and demand planning across the portfolio, including innovative medicines, vaccines, new launches and established products.
- Develop, challenge and refine commercial assumptions with Business Unit leaders and Finance, turning them into forecasts, scenarios and recommendations.
- Analyze variance between actual and forecast and tune predictive models to keep accuracy high.
- Manage a team of data analysts, data scientists and business planners, including performance evaluations and capability building.
- Convert forecasts and scenarios into demand plans that balance commercial ambition with supply feasibility, product availability and inventory risk.
- Own Japan's forecast submission process and represent Japan in regional demand discussions.
Required profile
- 10+ years in commercial (sales/marketing), business analytics/intelligence or business planning in pharma (Rx/ethical drugs) or FMCG.
- At least 4 years leading and managing a team of specialized professionals.
- University degree in Data Science, Computer Science, Statistics, Mathematics, Business, Economics or Finance, or equivalent practical experience.
- Good written and verbal English; bilingual Japanese/English preferred.
- Legal right to work in Japan.
Required skills
- Statistical modelling and simulation, sensitivity analysis and scenario analysis.
- Demand planning, S&OP / IBP, supply planning and MRP.
- Preferred: understanding of the pharmaceutical industry, commercial excellence, advanced analytics and machine learning concepts, and experience with pharmaceutical/medical data sources.
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