Head of Applied Machine Learning - Application Fraud
Leads an applied machine learning organization responsible for fraud detection and identity verification models, combining people management with hands-on technical direction. Requires extensive ML leadership, production modeling experience, strong Python skills, and expertise operating in sensitive risk-focused domains.
About the job
Responsibilities
- Directly manage and grow a team of applied ML scientists.
- Set engineering and modeling practices for the team.
- Own strategy and execution for the applied ML domain, including roadmap, priorities, resourcing, and results.
- Mentor the team on modeling and architecture decisions, review pull requests, and stay close to production systems.
- Partner with senior leadership, Product, Engineering, and Risk to set priorities and deliver results.
- Represent the domain in product strategy discussions.
- Own fraud detection and identity models across data acquisition, feature engineering, labeling, training, experimentation, deployment, monitoring, and iteration.
- Research emerging fraud patterns and build ML capabilities for identity verification and financial risk.
- Design analyses that inform product and business decisions.
- Guide the use of AI in team workflows and product development.
Requirements
- 10+ years of industry experience applying machine learning or statistics to real-world problems, or 7+ years with a relevant PhD.
- 6+ years of direct management experience leading machine learning or data science teams across two or more companies.
- Experience leading ML or data science teams in fraud, identity, fintech, banking, financial services, payments, or adjacent risk-focused domains is strongly preferred.
- Bachelor's, master's, or PhD in Computer Science, Statistics, Mathematics, Physics, or another quantitative discipline.
- Demonstrated success developing and deploying production machine learning models.
- Production-quality Python coding and testing experience.
- Strong practical machine learning and applied statistics knowledge.
- Experience owning a technical domain and driving measurable business impact.
- Fluency with modern LLMs and AI-assisted development workflows.
- Sound judgment with sensitive data, information security, and data governance constraints.
- Excellent communication with senior leadership and cross-functional stakeholders.
- Legal authorization to work in the United States and residence in the United States.
Technologies
- Python 3
- PostgreSQL
- AWS
- XGBoost
- scikit-learn
- pandas
- Elasticsearch
- OpenSearch
- Neo4j
- MLflow
- Flyte
- Modern LLM tooling
Compensation and Benefits
- $210,000-$260,000/year plus equity and benefits.
- Employer-paid group health insurance for employees and dependents.
- 401(k) plan with employer match.
- Flexible paid time off.
- Regular company-wide in-person events.
- Home office stipend.
Skills
Python, Postgres, AWS, Xgboost, scikit-learn, pandas, Elasticsearch, Opensearch, Neo4J, MLflow, Flyte, LLMs, Machine Learning, Applied Statistics, Fraud Detection
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