Senior Machine Learning Data Scientist, Extend

• Hands-on, proactive, and analytical professionals who are passionate about using data to solve complex, real-world problems
• Bachelor’s degree or higher in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Operations Research, Physics or related field
• 3+ years of work experience building and deploying machine learning systems into production
• Strong proficiency in Python and SQL
• Strong understanding of ML fundamentals: model selection, evaluation methodology, feature engineering, and common failure modes
• Hands-on experience with PyTorch, scikit-learn, and XGBoost (or similar gradient boosting frameworks)
• High attention to detail, strong intellectual curiosity, and a deep understanding of user behavior and fraud patterns
• Empathetic, humble, and collaborative team player
• Candidates must be located within the continental United States

Desirable

• The Fraud & Machine Learning team is the secret sauce behind Extend’s post-purchase protection platform. As a Senior ML Data Scientist, you will own the development of cutting-edge machine learning models based on signals and transactions from hundreds of millions of users to detect and prevent fraud, assess risk, and unlock business value
• You will drive the full data science lifecycle - from requirements and feature engineering through model development, evaluation, and monitoring
• You’ll partner closely with Product, Engineering, and our Fraud Intelligence team to translate messy data into scalable, production-grade ML systems that stop bad actors in their tracks. If you’re impact-driven and excited to tackle complex problems at the intersection of core machine learning and fraud prevention, you’ll thrive on our team!
• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure
• Translate complex fraud patterns into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches
• Design and maintain feature engineering pipelines for model development
• Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain
• Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies
• Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams