Bajaj Finserv
Website:
bajajfinserv.in
Job details:
Location Name: Pune Corporate Office - HO
Job Purpose
- 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.
- To build data-driven experimentation and feedback loops that enable evidence-based design thinking and rapid innovation.
- 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
- Leverage data (bureau, transactional, behavioral, funnel data) to identify opportunity areas for new product propositions.
- Conduct feasibility analysis to validate viability and scalability of proposed features/products.
- Build customer journey analytics frameworks (drop-off analysis, cohort tracking, behavioral segmentation).
- Design and execute rapid POCs and ML prototypes to test hypotheses.
- Develop end-to-end data pipelines and deploy production-grade ML models.
- Build experimentation frameworks to support product A/B testing and performance measurement.
- Develop dashboards and insight reports to guide product decision-making.
- Ensure strong governance, model documentation and validation processes.
A- Additional Accountabilities Pertaining To The Role
- Build ML use cases across credit risk, personalization, pricing, cross-sell and journey optimization.
- Integrate alternate data sources and bureau insights into product innovation.
- Embed LLM and agentic AI capabilities into customer journeys (where applicable).
- Mentor and guide data analysts within the unit.
Key Decisions / Dimensions
- Recommend go/no-go decisions based on POC analytics outcomes.
- Select modeling approaches and experimentation frameworks.
- Prioritize analytics use cases aligned to product roadmap.
- Define data architecture requirements in collaboration with Technology.
Major Challenges
A- Additional Accountabilities Pertaining To The Role
- Build ML use cases across credit risk, personalization, pricing, cross-sell and journey optimization.
- Integrate alternate data sources and bureau insights into product innovation.
- Embed LLM and agentic AI capabilities into customer journeys (where applicable).
- Mentor and guide data analysts within the unit.
- Balancing rapid experimentation with enterprise-grade scalability and compliance.
- Translating ambiguous business problems into structured data science use cases.
- Managing data quality, lineage and governance across multiple systems.
- Aligning analytics innovation with credit risk and regulatory constraints.
- Deploying ML models in production with minimal latency and high reliability.
Required Qualifications And Experience
- 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).
- 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.
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