Manager, Machine Learning

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. 

• 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*