Manager - Data Science - Analytics - Mumbai - Lower Parel - MM

1\. Business Problem Understanding \& Approach Development

• Engage with Business, Credit, Risk, Marketing, HR, and Audit teams to understand problem statements
• Participate in cross\-functional discussions to understand processes and identify analytical opportunities
• Translate business requirements into structured analytical approaches and solution frameworks

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2\. End\-to\-End Project Ownership

• Own delivery of analytics projects from problem definition to implementation and monitoring
• Manage timelines, stakeholder expectations, and delivery quality
• Ensure solutions are aligned with business objectives and decision\-making needs

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3\. Data Preparation \& Variable Creation

• Extract, clean, and prepare data from multiple sources
• Perform feature engineering and create relevant variables for model development
• Ensure data quality, consistency, and readiness for analysis

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4\. Model Development, AI Solutions \& Analytical Products

• Develop and deploy predictive, prescriptive, machine learning, and AI\-driven solutions across customer lifecycle, risk, collections, fraud detection, marketing, and operational use cases.
• Design, train, fine\-tune, evaluate, and optimize statistical, machine learning, deep learning, and Generative AI models.

Build solutions leveraging LLMs, RAG (Retrieval Augmented Generation), Agentic AI frameworks, NLP, and recommendation systems where applicable.

• Develop reusable analytical assets, APIs, model endpoints, and AI services for enterprise\-wide adoption.
• Ensure model explainability, fairness, robustness, scalability, and business relevance.

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5\. Business Analysis \& Insight Generation

• Conduct detailed data analysis to identify trends, patterns, and performance gaps
• Generate insights to support decision\-making across lifecycle stages
• Translate analytical outputs into clear, actionable business recommendations

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6\. Model Scoring, Monitoring \& MLOps

• Perform regular model scoring, performance monitoring, and stability assessments for deployed models.
• Monitor model drift, data drift, hallucination risks (for GenAI solutions), and overall model effectiveness.
• Establish model performance dashboards, automated alerts, and monitoring frameworks.