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Salary: USD 135,000 - 165,000 / annual
Extend is a post-purchase commerce platform that uses AI-driven solutions to help retailers enhance customer satisfaction and drive revenue growth. The company provides automated customer service, returns/exchange management, fulfillment automation, and fraud detection. With over 1,000 merchant partners across fashion, cosmetics, furniture, jewelry, electronics, and other industries, Extend is backed by prominent technology investors and headquartered in San Francisco.
As a Senior ML Data Scientist on the Fraud & Machine Learning team, you will own the full lifecycle of machine learning models that detect and prevent fraud at scale. You'll work with signals and transactions from hundreds of millions of users to assess risk and unlock business value.
Key responsibilities include: owning the model lifecycle from requirements through feature engineering, model development, evaluation, and monitoring; translating complex fraud patterns into well-framed ML solutions; designing and maintaining feature engineering pipelines; monitoring model quality in production and detecting data drift; partnering with Product, Engineering, and Fraud Intelligence teams to define fraud strategies; and fostering a culture of experimentation and collaboration.
Required qualifications: Bachelor's degree in a quantitative field (Mathematics, Statistics, Computer Science, Engineering, Operations Research, Physics, or related); 3+ years building and deploying ML systems in production; strong proficiency in Python and SQL; deep understanding of ML fundamentals including model selection, evaluation, feature engineering, and failure modes; hands-on experience with PyTorch, scikit-learn, and XGBoost or similar gradient boosting frameworks; attention to detail and intellectual curiosity; collaborative team player mindset; must be located in continental United States.
Preferred experience includes fraud detection or risk assessment systems, AWS cloud ML platforms (SageMaker), graph data and graph-based models (PyTorch Geometric), and model monitoring tools (Arize).
