Manager - Data Science - Analytics - Mumbai - Lower Parel - MM
The manager will lead end‑to‑end analytics projects, translating business problems from credit, risk, marketing and other domains into data‑driven solutions. They will own data extraction, cleaning, feature creation and model deployment while coordinating stakeholders and monitoring performance.
Skills required
About this role
. 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 clear, 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
