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Mid-level data-science role in Bangalore at a strong pharma brand with niche GenAI/regulatory focus.
Strong pharma/Medical Affairs and regulated (GxP) experience preferred, limiting cross-industry transferability.
Specific 5–6 years plus mandatory statistical, Python/R/SQL, and regulated-environment experience.
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Lead design and delivery of complex statistical and AI-driven analytical products for US Medical within Takeda’s Global Capability Center in India, setting methodological standards and acting as SME.
Develop and operationalize advanced statistical models, machine learning, and human-in-the-loop agentic-AI workflows to generate actionable insights that support Medical strategy and decision-making.
Partner with US Medical leaders and teams to translate scientific and business questions into rigorous analyses, communicate results effectively, and mentor analyst teams on applied AI and statistical methods.
Bachelor's Degree required in a quantitative field; advanced degree (MS/PhD) strongly preferred.
5–6 years of progressive quantitative experience in data science, statistics, or advanced analytics, ideally in Medical Affairs, Commercial, or pharmaceutical/life sciences domain, with significant focus on statistical modeling and applied machine learning.
Strong programming skills in Python, R, and SQL, with hands-on experience building and validating statistical and machine learning models.
Experience with GenAI, agentic-AI approaches, and human-in-the-loop workflows preferred; familiarity with pharmaceutical data ecosystems and operating in a regulated (GxP) environment preferred; experience in a GCC or offshore delivery model with willingness to overlap US time zones preferred.
Senior individual contributor with strong expertise in statistical methods, machine learning, and applied AI responsible for methodological rigor and reproducibility.
Experienced working in global or US-focused pharmaceutical or life sciences environments handling complex datasets and regulated data workflows.
Proactive strategic thinker who partners cross-functionally to define questions, translate data into business insights, and mentor junior analysts on statistical and AI modeling best practices.