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Mid‑level, popular Data Scientist role at a strong Tier‑1 pharma in Hyderabad, increasing candidate competition.
Strong ML and forecasting skills transferable, but pharma/regulatory forecasting experience narrows cross‑industry fit.
Explicit 2–5 years, mandatory forecasting/MLOps skills and regulated pharma context raise filter rigidity.
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Lead end-to-end development and scaling of advanced forecasting models using statistical, ML, and AI methods for commercial demand and planning.
Translate ambiguous business challenges into scalable analytical workflows and partner with stakeholders across Finance, Commercial, Market Access, and Analytics to influence strategic decision-making.
Design, deploy, and maintain enterprise forecasting platforms and ensure best practices in MLOps, while mentoring junior team members and adopting emerging AI/ML technologies.
2–5 years of experience in applied machine learning and data science problem-solving roles.
Bachelor’s, Master’s, or Ph.D. in Data Science, Computer Science, Statistics, Engineering, or related field.
Proven expertise in time-series forecasting techniques (ARIMA, Prophet, Holt-Winters) and Python-based data science workflows.
Experience with large structured and unstructured datasets (SQL, NoSQL) and MLOps tools (MLflow, Git, CI/CD, containerization).
Operates with high autonomy in ambiguous, fast-paced environments, independently managing forecasting workstreams.
Combines deep statistical and ML expertise with stakeholder leadership and strong communication skills to drive business impact.
Experienced in integrating advanced AI/ML and autonomous agent technologies into commercial forecasting and analytics workflows.