Requirements:

Modeling Depth

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. You should be able 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).

Technology Familiarity

Supervised learning, transfer leaning on machine learning as well as 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).

Nice-to-Have:

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.