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

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 &

- Analytical Solutions • Build models for use cases such as customer segmentation, credit risk assessment, early warning signals, collections prioritization, cross-sell and propensity modelling • Apply appropriate statistical and machine learning techniques • Ensure models are robust, interpretable, and aligned with business use 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 transparent, actionable business recommendations 6.

Model

Scoring &

- Performance Tracking • Perform regular model scoring (monthly / periodic) to classify accounts into high,



medium, and low risk categories • Track model performance and stability over time • Identify shifts in model behavior and recommend recalibration where required 7. Implementation &
- Deployment Coordination Work closely with IT and data teams to deploy models into production systems • Ensure smooth integration of models into business processes and workflows • Validate outputs post-deployment to ensure accuracy and usability 8. Monitoring &
- Continuous Improvement • Monitor performance of deployed models and analytics solutions • Assess whether models continue to be relevant and effective over time • Identify improvement areas and drive enhancements based on business feedback and data trends 9.

Stakeholder

Communication &

- Presentation • Present analysis, models, and insights to business and functional stakeholders • Explain methodologies and outputs in a clear and structured manner • Support decision-making through data-backed recommendations 10. Cross-Functional Collaboration • Work closely with Business, Credit, Risk, Marketing, HR, Audit, and IT teams • Ensure alignment between analytics solutions and operational execution • Act as a bridge between technical analytics and business application 11. Implementation &
- Deployment Coordination • Coordinate with business and technology teams for deployment and implementation of analytical solutions • Monitor implementation progress and ensure successful integration into business processes • Lead and guide junior team members in analytical problem solving, model development, and interpretation of business insights • Support capability building and knowledge sharing within the analytics function

📌 Data Science Manager - Analytics (Mumbai)
🏢 Tata Capital Finance
📍 Mumbai