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).

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).

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.