Senior Machine Learning Data Scientist

Extend

5h•$135,000 - $165,000•Remote

About The Position

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!

Requirements

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

Nice To Haves

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)

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