Role Summary We are looking for a Lead Data Science & Growth Analytics to drive data-led growth, credit modelling, and customer lifecycle optimization. The role will bridge risk, product, and growth, leveraging advanced analytics, ML, and LLMs to scale acquisition, improve conversion, and optimize portfolio profitability.

Key Responsibilities Credit & Risk Modelling :

Lead development of acquisition scorecards, behavioral models, and risk segmentation frameworks

Build and refine PD, LGD, and credit cost models

Optimize approval rates vs GCL in partnership with Risk teams

Drive champion-challenger and model recalibration strategies

Growth & Customer Analytics :

Build propensity, intent, and LTV models across products

Drive customer acquisition, funnel optimization, and cross-sell strategies

Analyze CAC vs LTV and improve marketing ROI

Design Next Best Action (NBA) frameworks for personalization

Product & Funnel Optimization :

Analyze end-to-end journey (acquisition KYC approval disbursement retention)

Identify and fix conversion bottlenecks

Drive A/B testing for pricing, UX, and credit strategies

Improve repeat usage and engagement across lending & payments products

Advanced Analytics & AI/ML :

Apply ML techniques (XGBoost, LightGBM, etc.) for risk and growth use cases

Leverage LLMs/NLP for customer insights, document parsing, and automation

Build feature engineering pipelines using bureau, banking, and behavioral data

Explore alternate data for underwriting and growth

Data Engineering & Deployment :





Work with large datasets using PySpark / distributed systems

Build scalable pipelines and deploy models (batch + real-time)

Monitor model performance, drift, and stability

Collaborate with Tech teams on MLOps and production systems

Stakeholder & Team Leadership :

Work closely with Risk, Product, Marketing, and Business teams

Translate business problems into analytical solutions

Lead and mentor a team of data scientists/analysts

Drive data-driven decision-making across the organization Required Skills Strong expertise in Python, SQL, PySpark

Hands-on experience in ML models (XGBoost, LightGBM, etc.)

Experience in credit risk modelling (scorecards, PD models)

Strong understanding of growth analytics (CAC, LTV, funnel metrics)

Exposure to LLMs/NLP use cases

Experience with data visualization tools (Power BI/Tableau) Preferred Qualifications Experience in Fintech / NBFC / Banking (Retail Lending)

Exposure to bureau and alternate data (banking, app behavior, etc.)

Experience with end-to-end model lifecycle (build ? deploy ? monitor)

Solid business acumen and problem-solving skills Key KPIs Disbursement / AUM growth

Approval rate optimization

GCL / Credit cost

CAC vs LTV improvement

Funnel conversion rates

Cross-sell and retention metrics Why Join Us Work on high-impact problems in digital lending and fintech

Opportunity to drive end-to-end data strategy

Fast-paced, ownership-driven environment

Exposure to cutting-edge AI/ML and LLM applications (ref:hirist.tech)

📌 Stashfin - Data Scientist/Lead - Credit Risk Modeling (Gurugram)
🏢 StashFin
📍 Gurugram