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Tier-1 employer and metro Hyderabad increase applicant density, but role seniority and niche forecasting reduce broad competition.
Requires deep forecasting, Bayesian and pharma commercial knowledge, so cross-industry transferability is limited.
Explicit 12–16 year requirements plus technical and domain mandates make shortlisting highly selective.
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Lead development of advanced statistical, Bayesian, causal, and machine learning forecasting models to support strategic decision-making across multiple business units.
Own end-to-end forecasting model lifecycle including data analysis, prototype development, deployment, monitoring, and explainability for reliable scenario planning.
Collaborate cross-functionally to integrate forecasting tools into business workflows facilitating risk-adjusted, near-, mid-, and long-term demand prediction and supply planning.
Bachelor's degree with 12 years enterprise data science experience OR Associate's degree with 14 years OR High school diploma/GED with 16 years experience.
Expertise in time-series forecasting, probabilistic programming, Bayesian and predictive modeling.
Strong skills in Python, SQL and data science libraries such as scikit-learn, PyMC, PyTorch, TensorFlow.
Work Experience Required: 12+ years of relevant data science in enterprise environments with principal-level influence.
Demonstrated success building and scaling forecasting platforms in biotech/pharma or related fields incorporating healthcare commercial knowledge (payer/provider dynamics, formulary access).
Operates strategically to bridge technical modeling with executive decision-making, able to communicate complex concepts and influence leadership.
Experience with causal inference, hierarchical/multi-level forecasting methods, and familiarity with ML Ops, CI/CD, and engineering best practices enabling scalable model deployment.