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Tier-1 pharma brand, metro location, mid-level data scientist role attracts high applicant competition.
Core ML and data science skills are transferable, but pharma domain and Bayesian expertise increase industry specificity.
Explicit 2–4 years requirement plus specific Python, Bayesian and pharma analytics skills increase shortlisting strictness.
Develop and deploy advanced predictive models and machine learning solutions for commercial decision-making in the pharmaceutical domain.
Create data visualizations and dashboards to communicate insights to senior stakeholders and support marketing and commercial analytics functions.
Collaborate with cross-functional teams to translate business needs into actionable AI/ML-powered tools and support strategic planning.
Master’s or Ph.D. in Data Science, Statistics, Computer Science, or related quantitative field.
2–4 years of experience in data science or advanced analytics, preferably in commercial pharma or healthcare.
Proficiency in Python (including pandas, scikit-learn, PyMC), SQL, and visualization tools such as Tableau or Power BI.
Experience working with pharma/healthcare datasets and commercial analytics concepts.
Strong expertise in statistical modeling, forecasting, segmentation, clustering, classification, regression, and Bayesian methods with practical application in pharma.
Experience designing and operationalizing ML pipelines, with familiarity or exposure to agentic AI frameworks a plus.
Comfortable working in dynamic, collaborative environments with multiple stakeholders to drive business transformation through analytical innovation.