Build and iterate on real-time fraud/risk models (e.g., gradient boosting and anomaly detection approaches) to score transactions during checkout and support Risk decisioning.
Own the full ML lifecycle for fraud detection models: data exploration, feature engineering, training, evaluation, deployment, monitoring, and continuous improvement.
Design robust evaluation strategies for rare-event, highly imbalanced data, including handling delayed/partial ground truth and defining metrics aligned with business constraints.
Partner closely with Risk, backend, and Data/Platform teams to productionize models behind an API, integrate with the risk engine, and improve model-driven decision flows (pre-/post-authorization).
Drive experimentation and feedback-loop initiatives to improve labels and model quality over time, while maintaining strong reliability, observability, and documentation.

Requirements

3+ years of experience as a Machine Learning Engineer (or in a similar applied ML role), ideally working with risk/fraud, anomaly detection, credit/default modeling, or other rare-event classification problems.
Strong Python skills and hands-on experience building and iterating on supervised ML models (e.g., Gradient Boosting/LightGBM or similar), including feature engineering and model evaluation.
Proven ability to design and run robust experimentation and evaluation under real-world constraints (imbalanced data, delayed/late-arriving labels, noisy or partial ground truth).
Experience taking models to production and supporting the full model lifecycle (training, deployment, monitoring, and iteration) in collaboration with engineering teams.
Solid knowledge of ML metrics and decisioning (precision/recall, thresholding, calibration, offline vs. online performance) and how they translate into business outcomes.
Familiarity with modern MLOps tooling and practices (e.g., MLflow) and working with feature stores (Databricks Feature Store or alternatives).
Nice to have: experience with real-time / streaming feature pipelines or infrastructure (e.g., Kafka, Flink, Feast) and building low-latency model services/APIs for real-time scoring.

Benefits

Opportunity to join our Employee Stock Options program.
Opportunity to help scale a unique product.
Various bonus systems: performance-based, referral, additional paid leave, personal learning budget.
Paid volunteering opportunities.
Work location of your choice: office, remote, opportunity to work and travel.
Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Machine LearningPythonGradient BoostingLightGBMFeature EngineeringModel EvaluationAnomaly DetectionRisk ModelingSupervised LearningImbalanced Data

Soft Skills

CollaborationExperimentationProblem SolvingDocumentationReliabilityObservability