Senior Data Scientist Digital Lending

Cutshort; Mumbai; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Cutshort is seeking a data science professional to own end-to-end credit and fraud data science for digital lending. You will build and deploy scorecards, engineer features from bureau data and alt-data, and shape a rules-based engine with product and engineering collaborators.

You will drive portfolio risk analytics, monitor performance, and push evidence-based policy changes. Strong SQL/Python skills and experience in credit modelling are required.

Qualifications

• 5+ years overall experience in data science/analytics.
• Digital lending: minimum 3 years hands-on in digital lending/consumer credit with models.
• Scorecards/models: Built and deployed at least one credit scorecard into live BRE/LOS.
• Bureau: Parsed and engineered features from raw bureau files (CRIF/CIBIL/Experian JSON/XML).
• Non-starter models: Fraud/First Payment Default modelling 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 signals for underwriting or fraud.
• Stack: Strong SQL + Python (pandas, sklearn, LightGBM/XGBoost, statsmodels).
• Communication: Comfortable debating policy with founders using data.

Responsibilities

• Build and maintain credit scorecards and models for FTB and Repeat Borrowers.
• Engineer features from raw bureau JSON and alt-data.
• Design, validate, and ship models with clear lift/capture and trade-offs.
• Own portfolio risk analytics and monitoring.
• Build fraud signals and prioritise fraud backlog into production.
• Partner with engineering to productionise features and models.
• Challenge and refine existing tier/ladder policy with evidence.

Skills

Data science

Pandas

Scikit-Learn

XGBoost

SQL

Tools

Airflow

S3

PostgreSQL

DynamoDB

Job description

Job Description:

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 clear 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.

Skills:- Data Science, pandas, Scikit-Learn, XGBoost and SQL