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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1. 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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1. 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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1. 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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1. 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 explicit, actionable business recommendations

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1. 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.

📌 Manager - Data Science - Analytics - Mumbai - Lower Parel - MM
🏢 Tata Capital
📍 Mumbai