Job description

About Extend: Extend is revolutionizing the post-purchase experience for retailers and their customers by providing merchants with AI-driven solutions that enhance customer satisfaction and drive revenue growth. Our comprehensive platform offers automated customer service handling, seamless returns/exchange management, end-to-end automated fulfillment, and product protection and shipping protection alongside Extend's best-in-class fraud detection. By integrating leading-edge technology with exceptional customer service, Extend empowers businesses to build trust and loyalty among consumers while reducing costs and increasing profits. Today, Extend works with more than 1,000 leading merchant partners across industries, including fashion/apparel, cosmetics, furniture, jewelry, consumer electronics, auto parts, sports and fitness, and much more. Extend is backed by some of the most prominent technology investors in the industry, and our headquarters is in downtown San Francisco. About the Role: 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! What You’ll Be Doing: 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 What We’re Looking For: Required: 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 Preferred: 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) Estimated Pay Range: $135,000 - $165,000 per year salaried* * The target base salary range for this position is listed above. Individual salaries are determined based on a number of factors including, but not limited to, job-related knowledge, skills and experience. Life at Extend: Working with a great team from diverse backgrounds in a collaborative and supportive environment. Competitive salary based on experience, with 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. Extend CCPA HR Notice

Requirements

Must-haves

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
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)
Working with a great team from diverse backgrounds in a collaborative and supportive environment.
Stock in an early-stage startup growing quickly.
Extend CCPA HR Notice

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlgopythonsqlaws

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off