[Remote] Senior Machine Learning Data Scientist

Note: The job is a remote job and is open to candidates in USA. Extend is revolutionizing the post-purchase experience for retailers with AI-driven solutions that enhance customer satisfaction and drive revenue growth. As a Senior ML Data Scientist, you will own the development of machine learning models to detect and prevent fraud, assess risk, and unlock business value, partnering with various teams to translate data into scalable ML systems.

Responsibilities

• 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

Skills

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

Benefits

• Full medical and dental & vision benefits
• Stock in an early-stage startup growing quickly.
• Generous, flexible paid time off policy.
• 401(k) with Financial Guidance from Morgan Stanley.

Company Overview