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Broad ML/GenAI skills, commercial data scientist title, Bangalore metro, and strong global employer increase applicant competition.
Core ML, MLOps and GenAI skills transfer across industries, though pharma/commercial domain experience is advantageous.
Senior-level ML/GenAI production expertise and MLOps plus advanced degree preferences create stringent technical and background filters.
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Develop and deploy end-to-end AI/ML solutions including forecasting, econometrics, and GenAI applications to drive commercial decision-making.
Translate commercial business questions into practical data science models and take them through production using MLOps best practices.
Collaborate closely with commercial stakeholders, domain experts, and AI engineers to embed solutions and lead technical projects including mentoring team members.
Experience: Several years in developing, deploying, or supporting data science, machine learning, statistical or AI models for commercial or business data.
Mandatory technical skills include strong proficiency in Python, SQL, coding best practices (Git, documentation, reusable and tested code), and model validation/interpretation.
Degree: MSc, PhD, or equivalent practical experience in statistics, econometrics, data science, mathematics, computer science, engineering or related quantitative field.
Work Experience Required: Several years; Notice Period: Not explicitly mentioned in the JD.
Strong expertise in at least one of: statistical modeling, econometrics, forecasting, causal inference, segmentation, applied ML, model production, MLOps, or GenAI engineering (LLM, RAG, agentic workflows).
Demonstrates AI-native working style with familiarity in AI-assisted development tools and cloud-native platforms (Databricks, Snowflake, Azure).
Experience leading complex, production-focused commercial AI projects and collaborating cross-functionally with stakeholders and AI engineers.