Manager - Data Science - Analytics - Mumbai - Lower Parel - MM
Tata Capital · Mumbai
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
________________________________________
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
________________________________________
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
________________________________________
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.
________________________________________
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
________________________________________
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.