Senior Machine Learning Engineer (Fraud)
A
AffirmFinancial Technology
Remote CanadaFull-TimeSenior
SalaryCAN base pay range per year: $153,000 - $213,000
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonKubeflowMachine LearningMLFlowPyTorchAirflowSpark
Requirements
- 6+ years experience researching, training, tuning and launching ML models at scale (relevant PhD can count for up to 2 years).
- Track record of delivering high impact machine learning models in a low latency live setting.
- Strong Python skills and experience writing production-quality code.
- Experience building and evaluating models for tabular classification problems (e.g., LightGBM, XGBoost, CatBoost).
- Experience with a deep learning framework (PyTorch preferred).
- Experience with distributed data processing or parallel compute frameworks (Spark, Ray, or Dask).
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow).
- Proficient in using AI-powered developer tools to accelerate iteration and debugging.
- Ability to navigate large code bases, debug existing code, and perform effective code reviews.
- Strong verbal and written communication skills for cross-functional collaboration.
Responsibilities
- Lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data.
- Build and scale feature pipelines and training datasets from proprietary and third-party signals.
- Prototype new modeling ideas, run offline experiments, and drive performing approaches into production with risk controls.
- Productionize models by integrating them into batch or real-time decision systems while improving reliability and latency.
- Instrument and monitor model and data health and define retraining and backtesting workflows.
- Identify and implement foundational improvements to how the team builds models.
- Collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements and evaluate tradeoffs.
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