Head - Data Scientist - FinTech

Head - Data Scientist

Experience: 4+ years | Industry: Fintech / Lending | Location: Bangalore

Role Overview:

1. Own and lead the Data Science & Analytics unit at end-to-end.

2. Build and scale Data Analytics + marketing analytics capabilities (MMM, channel mix, attribution/measurement, Income estimation Model, Base selection Strategy).

3. Drive funnel analytics and optimization across Base - Response - Lead - approval - disbursal.

4. Define, track, and improve core business metrics with strong governance (single source of truth).

5. Partner with senior leadership to convert business problems into data-backed decisions and execution plans.

6. Build team processes: prioritization, experimentation discipline, reporting standards, and stakeholder communication.

7. Own and manage the data engineering / data pipeline function-data ingestion, transformations, quality checks, and reliability of datasets powering analytics and reporting.

8. Own dashboards and reporting outputs end-to-end-definition, design standards, adoption, and ensuring dashboards drive action (not just reporting).

What is required from you:

1. 4+ years of experience in Data Science / Analytics in fintech/lending.

2. Hands-on experience working with Indian bureau data (e.g., Experian/CRIF/Equifax/CIBIL) and translating bureau signals into usable features/insights.

3. Proven ownership of metrics and dashboards (definitions, governance, data quality, and business adoption).

4. Experience building/leading marketing models: marketing mix modelling (MMM), channel mix optimization, ROI/incrementality measurement, and base selection strategies.

5. Demonstrated ability to identify bottlenecks in funnels, run analyses, and drive measurable improvements.

6. Prior experience managing a team (analysts/data scientists) and owning deliverables end-to- end.

7. Strong accountability mindset: can take responsibility for outcomes, not just analysis.

Skills required:

- Strong understanding of growth funnels, cohorting, segmentation, attribution, and KPI design.

- Model building experience: propensity, segmentation, MMM/channel mix, and monitoring frameworks.

- Strong communication: can present insights clearly to leadership and align cross-functional teams.

- Execution-oriented: able to translate analysis into product/process changes and track impact.