Principal / Staff Data Scientist
RemoteUnited States
Job Summary
Drive innovation across the global ML ecosystem by architecting advanced machine learning solutions and leading complex initiatives that shape technical direction. Own models in production from deployment through monitoring, drift detection, and retraining, ensuring systems meet real latency budgets while handling billions of transactions. Mentor junior engineers worldwide to elevate engineering standards and guide teams in building scalable, production-ready systems. Manage classical ML for fraud/anomaly detection, recommendation, and churn/LTV using gradient boosting, deep learning, and graph-based models. Leverage MLOps foundations including feature stores, experiment tracking, and continuous training pipelines, while optimizing model serving and inference.
Required Qualifications
- Advanced Degree in Statistics, machine learning or related areas
- Experience in statistics/ML expertise with a track record of leading high-impact data science initiatives at scale of billions of transactions
- Hands-on experience creating, training and fine-tuning models not just integrating hosted model APIs
- Ability to walk through the data, the objective, what broke, and the before/after evaluation numbers, and why the model did not perform as expected
- Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks
- Production experience with classical ML for fraud/anomaly detection, recommendation, or churn/LTV (gradient boosting, deep learning, graph-based models)
- Supervised learning
- Transfer learning on machine learning
- Neural networks
- Basic LLM knowledge, especially how to use it and where not to use it
- MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines
- Model serving and inference optimization (vLLM-class serving, quantization)
Desired Qualifications
- Publications, conference talks, or recognized open-source contributions to training/eval tooling
- Graph-based fraud detection (fraud rings, device/account linkage)
- Gaming, payments, fraud, advertising domain experience
- Hands-on, up-to-date experience with modern AI tools (e.g., Claude, Copilot, Cursor) for code generation, review, and accelerating day-to-day engineering work
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