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

Tata Capital; Mumbai; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Tata Capital invites you to join its analytics team to lead end-to-end data projects across credit, risk, marketing, HR and operations. You will translate business needs into rigorous analytical frameworks and deliver production-ready models.

You will own data prep, feature engineering, model development, deployment, scoring, monitoring and stakeholder communication, driving data-driven decisions across lifecycle stages and continuous improvement.

Responsibilities

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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

Job description

Job Description:

. 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