Best suited to mid-level machine learning managers with fraud detection experience who thrive in remote US roles requiring cross-functional collaboration.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Extend via Greenhouse
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:
You will lead a team of ML data scientists on the Fraud and ML team, owning the development and quality of Extend's machine learning models across fraud detection, risk assessment, and identity resolution. You'll guide your team through the full data science lifecycle, from requirements and experimentation through model development, evaluation, and monitoring. You’ll partner closely with Product and Engineering on integrating ML models into our product and with our Fraud Intelligence team to continuously improve our fraud detection capabilities.
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 business problems 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
Drive experiment design and statistical rigor: ensuring models are evaluated with sound methodology before and after launch
Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain
Cultivate a culture of learning and collaboration within and across partner teams
Perform design and code reviews to raise the technical excellence bar
Hire, mentor, and coach data scientists
What We’re Looking For:
Required:
6+ years of work experience building and deploying machine learning systems into production
2+ years experience mentoring and managing ML teams
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)
Strong people leadership skills with the ability to develop ML talent
Excellent stakeholder management, with a track record of working cross-functionally to deliver results
Empathy and humility
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: $180,000-$210,000 per year salaried*
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.
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Source: Extend careers (Greenhouse)