Role: Data Analyst / Analytics Engineer About the Company A high-growth fintech platform is scaling two core data initiatives: a credit bureau intelligence engine that turns financial signals into automated underwriting logic, and an embedded health membership product distributed across its large lending base. This role sits at the intersection of credit data, user behavior, and multi-channel campaign analytics to power real-time eligibility engines, customer targeting models, and product-mapping triggers. Role Overview A hands-on, build-from-scratch position focused on turning raw data into working scripts, decision rules, and evaluation models without reliance on pre-built dashboards. Key Responsibilities
Credit Bureau Data Analysis: Work with raw bureau files (scores, tradeline histories, account classifications, enquiry velocity) to build risk segmentations, scoring rules, and lending eligibility logic.
Rule Engine & Tool Development: Build and maintain client-side eligibility and Business Rule Engine (BRE) frameworks by converting partner credit policies into testable code.
Campaign & Funnel Analytics: Analyze communication logs (WhatsApp, voice, call center CDR data) to pinpoint high-fit borrower segments, conversion drivers, and funnel drop-offs.
Signal-to-Trigger Mapping: Build algorithmic triggers linking credit behavior changes to contextual health and financial protection offerings.
Pipelines & Experimentation: Develop repeatable Python and SQL workflows for A/B testing, cohort tracking, and executive performance reviews.
Data Hygiene & Structuring: Ingest, clean, and standardize large-scale CRM files, call logs, and bureau extracts for cross-functional product and growth teams.
Candidate Profile
Experience: 0–2 years in Data Analytics, Data Science, or a related analytical discipline (internships included).
Technical Stack: Strong proficiency in Python (pandas, numpy) and SQL, alongside spreadsheet modeling for rapid analysis.
Analytical Skills: Solid grasp of statistics, cohort dynamics, and performance metrics (precision, approval bands, conversion rates).
Domain Curiosity: High interest in credit bureau data, digital lending, and fintech/insurtech intersections.
Execution: Comfort with unstructured, messy datasets and the ability to articulate technical insights to business stakeholders.
Nice-to-Haves: Exposure to classification models, lightweight internal dashboards (HTML/JS), or lending/insurance underwriting logic.
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