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The Role

Own end-to-end credit & fraud data science: feature engineering from raw bureau JSON ,SMS,DEVICE, scorecard / model development, Business Rule Engine (BRE) design, monitoring, and partnering with product/engineering to put rules live. You will work directly with the existing DS team,Tech,product and founders — decisions are data-backed and debated.

What you will own

1. Build and maintain credit scorecards and models for FTB and Repeat Borrowers (Xgboost, Random forest, Support Vector Machine Models, ensemble models, challenger models).
2. Engineer features from raw CRIF (or equivalent) bureau JSON — tradelines, enquiries, DPD histories, identity matches — and from raw SMS / FinBox alt-data (collections, rejections, salary, app footprint).
3. Design, validate, and ship Models: hard rejects, soft flags, amount caps — with clear lift/capture

/ approval trade-offs.

1. Own portfolio risk analytics: vintage / DPD / non-starter / POS bad-rate monitoring; propose tier pauses, cool-offs, and ladder-up changes.
2. Build fraud signals (device, SIM/OTP, mule, ring, post-disbursal disappearance) and help prioritise the fraud PRD backlog into production.
3. Partner with engineering to productionise features, rules, and models (Watchtower-style shadow underwriting, policy index, monitoring dashboards).
4. Challenge and refine existing tier/ladder policy with evidence; communicate clearly to founders and business.

Required experience

• Tenure: 5+ years overall experience in data science/analytics.
• Digital lending: Minimum 3 years hands-on in digital lending/consumer credit (NBFC, fintech lender, digital/STPL/) who has built models themselves.
• Scorecards/models: Built and deployed at least one credit scorecard (first-time borrower or repeat borrower, or combined model) into a live BRE / LOS. Should improve approval–bad-rate trade-offs from production experience.
• Bureau: Parsed and engineered features from raw bureau files (CRIF / CIBIL / Experian JSON or XML) — not only vendor-precomputed attributes.
• Non-starter models: Fraud/non-starter / First Payment default modelling experience in short-tenure lending.
• Limit Assignment: Experience with repeat-borrower ladder / limit-management policies.
• Monitoring and QC: Shadow underwriting/champion–challenger frameworks.
• Alt-data: Worked with SMS / alt-data / device / AA signals for underwriting or fraud (FinBox, similar vendors, or in-house SMS parsing).
• Stack: Strong SQL + Python (pandas, sklearn/Logistic / lightgbm/Xgboost/randomforest, statsmodels). Able to write production-quality notebooks and scripts, not just slide decks.
• Communication: Comfortable debating policy with founders/credit heads using data; owns the "show me the evidence" conversation.

Nice to have:

• Feature stores, Airflow/cron pipelines, S3 + Postgres + DynamoDB.
• Prior Experience: Prior work at a zero-to-one digital lender or STPL product.

What success looks like in 6 months

• A documented feature dictionary from raw bureau + SMS with IV/KS ranking.
• At least one new scorecard/model live with clear expected vs observed bad-rate impact.
• Non-starter / First Payment Defaults monitoring with actionable rule recommendations and clear demonstrated improvements in defaults
• Credible pushback on weak policy ideas — backed by analysis, not opinion.

What You’ll Do

● Partner with Product to spot high-leverage ML opportunities tied to business

metrics.

● Wrangle large structured and unstructured datasets; build reliable features and

data contracts.

● Build and ship models to:

○ Enhance customer experiences and personalization

○ Boost revenue via pricing/discount optimization

○ Power user-to-user discovery and ranking (matchmaking at scale)

○ Detect and block fraud/risk in real time

○ Score conversion/churn/acceptance propensity for targeted actions

● Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.

● Design and run A/B tests with guardrails.

● Build monitoring for model/data drift and business KPIs

What We’re Looking For

● 2–4 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.

● Proven, hands-on success in at least two (preferably 3–4) of the following:

○ Recommender systems (retrieval + ranking, NDCG/Recall, online lift;

bandits a plus)

○ Fraud/risk detection (severe class imbalance, PR-AUC)

○ Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs,

guardrails/simulation)

○ Propensity models (payment/churn)

● Programming: strong Python and SQL; solid git, Docker, CI/CD.

● Cloud and data: experience with AWS or GCP; familiarity with

warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).

● ML breadth: recommender systems, NLP or user profiling, anomaly detection.

● Communication: clear storytelling with data; can align stakeholders and drive decisions.