Senior Machine Learning Data Scientist

Role Overview

Join Extend as a Senior Machine Learning Data Scientist on a full data science lifecycle: building, deploying, evaluating, and monitoring ML models to detect/prevent fraud and assess risk.

Key Responsibilities

• Own the end-to-end data science lifecycle, from feature engineering through model development, evaluation, and deployment/monitoring.
• Collaborate with product, engineering, and fraud intelligence teams to translate complex data into scalable ML systems.
• Manage the full model lifecycle: requirements, experimentation, development, evaluation, and model “cards”/documentation.

Requirements

• 3+ years building and deploying machine learning systems into production.
• Strong ML fundamentals: model selection, evaluation methodology, feature engineering, and common failure modes.
• Hands-on experience with Python and SQL.
• Hands-on with PyTorch, scikit-learn, and XGBoost (or similar gradient boosting frameworks).
• Proactive, analytical, and collaborative mindset; strong attention to detail.
• Located in the continental United States.
• Bachelor’s degree or higher in a quantitative field (e.g., Math, Stats, CS, Engineering, Operations Research, Physics).

Nice-to-Haves / Additional Skills

• Experience building fraud detection or risk assessment systems.
• Experience with cloud ML platforms, especially AWS (e.g., SageMaker).
• Experience with model monitoring/observability (e.g., Arize).
• Experience with graph data and graph-based models (e.g., PyTorch Geometric).