Data Scientist Junior, Sign 3
Cedar Management Consulting · Gurgaon, India ·
- Seniority
- Junior
- Employment
- Full time
- Category
- Data science
- Experience
- 1+ years
Cedar Management Consulting · Gurgaon, India ·
checkout.com · Tel Aviv
Transmit Security · Tel Aviv-Yafo, Gush Dan, Israel
rain · New York, NY
tonicai · Remote
The junior data scientist will build and deploy binary fraud classifiers using Python, XGBoost/LightGBM, and SQL, engineer features from diverse signals, monitor model health, and collaborate with engineering and product teams.
• Binary classifiers under heavy class
imbalance. Fraud base rates are low. You will build GBMs (XGBoost,
LightGBM) and shallow neural models that have to perform at the low-FPR
operating points clients actually care about — not at default thresholds.
• Feature engineering across signal families. Device,
behavioral, location, image, telecom and alternate-data signals, plus velocity
aggregations across multiple time windows. You will learn to turn raw telemetry
into features that survive adversarial drift.
• Anomaly and unsupervised detection. Isolation
Forest, auto encoders, PCA residuals, peer-group outliers — for the cold-start
cases where labeled fraud is weeks away.
• Graph features (growing scope).Contribute
to our graph-based fraud and community-detection work. You will start with
graph-derived tabular features and can grow deeper into graph modelling if the
work suits you.
• Deployment. Package models for our
real-time decisions path. Understand latency budgets and feature availability
at scoring time (no leakage, no missing features in production).
• Monitoring. PSI, CSI, score-distribution
drift, feature drift, fraud-capture curves by client. Own the weekly
model-health review for at least one deployed model within six months.
• Threshold economics. Work with clients to
translate fraud capture ↔ customer-friction trade-offs into operating points
they can sign off on. This is where most fraud models actually fail, and where
you will learn the most.
• With engineering, on feature pipelines,
offline/online parity and real-time inference.
• With product and clients, on what a model
is actually allowed to flag, block or auto-approve. You will sit in review
calls with bank fraud heads.
• 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.
• 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.
• Compensation benchmark to top-quartile Indian
FinTech; ESOPs for all full-time employees with a clear vesting schedule.
• Health insurance for you and your family, and a
meaningful learning & conference budget.
• Access to real, messy, high-volume Indian
financial data and the compute to do something interesting with it.
fticonsulting · London, United Kingdom