Manager - Data Science - Analytics - Mumbai - Lower Parel - MM
Tata Capital · Banking & Financial Services
- Mumbai, Maharashtra, India
- On-site
- Posted yesterday
- Apply by 17 Jan 2027
- Data & AI
About the job
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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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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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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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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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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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.