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Tier-1 brand and metro location increase competition, but senior, niche forecasting/ML focus moderates density.
Specialized biotech forecasting, causal inference, and pharma commercial knowledge limit cross-industry transferability.
Explicit 12+–16 years and deep forecasting/ML domain requirements make hiring filters stringent.
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Lead development of advanced statistical, Bayesian, causal, and machine learning forecasting models across multiple horizons to improve strategic decision making.
Own end-to-end modeling lifecycle including deployment, monitoring, and explainability, and build self-service forecasting tools for near real-time business action.
Collaborate with cross-functional teams (Commercial, Operations, Supply Chain, Finance, Technology) to integrate forecasts into critical business workflows and mentor junior team members.
Bachelor’s degree with minimum 12 years data science experience in enterprise environments (or Associate’s degree with 14 years, or High school diploma with 16 years).
Deep expertise in time-series forecasting, probabilistic programming, Bayesian and predictive modeling with practical model delivery experience.
Proficiency with Python, SQL, and data science libraries (scikit-learn, PyMC, Pytorch, Tensorflow).
Work Experience Required: Minimum 12 years applying data science in enterprise environments at principal level or equivalent depth.
Experienced leader skilled in building scalable forecasting platforms with deep knowledge of biotech/pharma commercial environment (payer/provider dynamics, formulary access).
Strong scientific and technical acumen in advanced forecasting methods including causal inference and hierarchical forecasting, combined with practical deployment skills (ML Ops, CI/CD).
Effective communicator capable of influencing executives and aligning cross-functional stakeholders through clear explanation of complex technical concepts and tradeoffs.