Role - Data Scientist 5 years

Mandatory: 2–3 years of experience working on Short-Term Personal Loan (STPL) products

Location - Mumbai Andheri (Full-Time)

About the company

We are a listed NBFC (BSE & NSE) with ~40 years of history. Alongside a co-lending / fintech partner book, we run — our own B2C short-term personal loan (STPL) product launched in late 2025. Ticket sizes are ₹1,000–₹15,000, and tenures are 3–6 months. We are a team (~35–40 people) operating like a startup inside a regulated public listed NBFC.

The Data Science team owns Risk, policy, fraud signals, scorecards, and portfolio & overall analytics. We are hiring a Senior Data Scientist who has built and shipped credit decisioning systems (scorecards, BRE, feature pipelines) in digital lending.

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

- Build and maintain credit scorecards and models for FTB and Repeat Borrowers (Xgboost, Random forest, Support Vector Machine Models, ensemble models, challenger models).
- 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).
- Design, validate, and ship Models: hard rejects, soft flags,



amount caps — with explicit lift/capture / approval trade-offs.
- Own portfolio risk analytics: vintage / DPD / non-starter / POS bad-rate monitoring; propose tier pauses, cool-offs, and ladder-up changes.
- Build fraud signals (device, SIM/OTP, mule, ring, post-disbursal disappearance) and help prioritise the fraud PRD backlog into production.
- Partner with engineering to productionise features, rules, and models (Watchtower-style shadow underwriting, policy index, monitoring dashboards).
- 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.

Hiring process

- Screening call and technical deep dive (role fit, digital lending depth).
- In-person technical deep dive case study portfolio discussion overall
- Founder/culture Technical round.
- Offer.

📌 Data Scientist (Mumbai)
🏢 HiroJet
📍 Mumbai