Lead fraud prediction models using tabular, graph, and behavioral data
Build and scale feature pipelines and training datasets
Prototype modeling ideas; offline experiments; risk-managed production
Productionize models: batch/real-time decisions; improve latency
Instrument and monitor model/data health; define retraining/backtesting workflows
Collaborate across Engineering, Fraud Analytics, Product, ML Platform

🎯 Requirements

6+ years ML model experience; PhD counts up to 2
Low-latency ML in production
Strong Python; production-quality code
Tabular classification models (LightGBM/XGBoost/CatBoost)
PyTorch experience
Distributed data processing + ML lifecycle tooling (Spark; Kubeflow/Airflow/MLflow)

🎁 Benefits

Remote-first culture; mostly remote within country
Health/dental/vision coverage for you and dependents
Flexible Spending Wallets for tech, meals, lifestyle
ESPP and equity rewards

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