Lead a team of machine learning data scientists, overseeing model development for fraud detection and risk assessment, and ensuring quality throughout the model lifecycle while mentoring the team.
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.
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
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.
Extend CCPA HR Notice
Similar Jobs
Artificial Intelligence • Fintech • Healthtech • Software
Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.
Top Skills:
Machine LearningPythonSQL
Big Data • Fintech • Mobile • Payments • Financial Services
Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.
Top Skills:
Data PipelinesDeep LearningGradient-Boosted TreesMachine LearningRepresentation LearningScalable SystemsTransformer-Based Models
Big Data • Fintech • Mobile • Payments • Financial Services
Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.
Top Skills:
AirflowCatboostKubeflowLightgbmMachine LearningMlflowPythonPyTorchSparkXgboost
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
