Data Scientist Junior, Sign 3
On-siteGurugram, Haryana, India
Job Summary
Build binary classifiers and unsupervised anomaly detectors using GBMs, neural models, and graph features to address heavy class imbalance in fraud detection. Engineer features across device, behavioral, and alternate-data signals, then package models for real-time inference while managing latency budgets and feature availability. Monitor deployed systems for score and feature drift, review model health weekly, and collaborate with engineering to ensure offline/online parity. Partner with product and client fraud heads to translate capture versus friction trade-offs into operating points. Apply statistical intuition to select thresholds that satisfy client risk requirements.
Required Qualifications
- 1–3 years of hands-on ML experience building supervised models on real data (not only coursework or Kaggle)
- Strong Python and SQL
- Comfortable with pandas/Polar, scikit-learn, XGBoost or LightGBM, and writing non-trivial SQL against large tables
- Statistical intuition
- You know why accuracy is the wrong metric for fraud, can explain ROC vs PR curves, and understand calibration and threshold selection
- Engineering hygiene
- Git, code review, reproducible experiments, basic CI
- Your models should run when someone else checks out your branch
- Clear writing
- You can write a one-page memo that a non-technical product manager and a fraud-ops lead both walk away understanding
Desired Qualifications
- Exposure to fraud, risk, credit, AML or payments data — at a bank, FinTech, card network or risk-tech vendor
- Experience with imbalanced-class techniques beyond naive resampling (focal loss, cost-sensitive learning, threshold-moving, calibration)
- Experience with real-time feature serving, model monitoring or MLOps tooling (MLflow, Feast, SageMaker, Vertex, or equivalents)
- Exposure to graph data, NLP/NER, or geospatial features
- A public repo, paper, competition placement or blog post that shows how you think
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