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

Job Description

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.