Cluster Manager - PL - Design Thinking

Job Purpose 1.
 To leverage advanced analytics, machine learning and data science to uncover hidden customer opportunities and validate viability of new product propositions within B2C financial services.
2. To build data-driven experimentation and feedback loops that enable evidence-based design thinking and rapid innovation.
3. To lead end-to-end development of analytics use cases - from exploratory analysis and POCs to production-grade ML pipelines powering customer journeys.
Duties and Responsibilities A- Minimum required Accountabilities for this role 1.
 Leverage data (bureau, transactional, behavioral, funnel data) to identify opportunity areas for new product propositions.
2. Conduct feasibility analysis to validate viability and scalability of proposed features/products.
3. Build customer journey analytics frameworks (drop-off analysis, cohort tracking, behavioral segmentation).
4. Design and execute rapid POCs and ML prototypes to test hypotheses.
5. Develop end-to-end data pipelines and deploy production-grade ML models.
6. Build experimentation frameworks to support product A/B testing and performance measurement.
7. Develop dashboards and insight reports to guide product decision-making.
8. Ensure strong governance, model documentation and validation processes.
A- Additional Accountabilities pertaining to the role 1.
 Build ML use cases across credit risk, personalization, pricing, cross-sell and journey optimization.
2. Integrate alternate data sources and bureau insights into product innovation.
3. Embed LLM and agentic AI capabilities into customer journeys (where applicable).
4. Mentor and guide data analysts within the unit.
Key Decisions / Dimensions 1.
 Recommend go/no-go decisions based on POC analytics outcomes.
2. Select modeling approaches and experimentation frameworks.
3. Prioritize analytics use cases aligned to product roadmap.
4. Define data architecture requirements in collaboration with Technology.
Major Challenges A- Minimum required Accountabilities for this role 1.
 Leverage data (bureau, transactional, behavioral, funnel data) to identify opportunity areas for new product propositions.
2. Conduct feasibility analysis to validate viability and scalability of proposed features/products.
3. Build customer journey analytics frameworks (drop-off analysis, cohort tracking, behavioral segmentation).
4. Design and execute rapid POCs and ML prototypes to test hypotheses.
5. Develop end-to-end data pipelines and deploy production-grade ML models.
6. Build experimentation frameworks to support product A/B testing and performance measurement.
7. Develop dashboards and insight reports to guide product decision-making.
8. Ensure strong governance, model documentation and validation processes.
A- Additional Accountabilities pertaining to the role 1.
 Build ML use cases across credit risk, personalization, pricing, cross-sell and journey optimization.
2. Integrate alternate data sources and bureau insights into product innovation.
3. Embed LLM and agentic AI capabilities into customer journeys (where applicable).
4. Mentor and guide data analysts within the unit.
1. Balancing rapid experimentation with enterprise-grade scalability and compliance.
2. Translating ambiguous business problems into structured data science use cases.
3. Managing data quality, lineage and governance across multiple systems.
4. Aligning analytics innovation with credit risk and regulatory constraints.
5. Deploying ML models in production with minimal latency and high reliability.
Required Qualifications and Experience a) Qualifications • Post Graduates with relevant experience of 5-7 years in Data Analytics o B.
Tech / M.Tech / MBA (Analytics / Data Science / Statistics / Computer Science preferred).
b) Work Experience • 5-7 years of hands-on experience in Data Science / Advanced Analytics • Strong proficiency in Python, Spark, PySpark and SQL.
• Experience building end-to-end ML pipelines and deploying production-grade models.
• Expertise in customer journey analytics, segmentation and experimentation frameworks.
• Exposure to credit bureau data, lending analytics or financial services preferred.
• Familiarity with LLMs, Generative AI and agentic AI frameworks is an advantage.
• Strong problem structuring, stakeholder management and business translation skills.