Head of Applied Machine Learning - Application Fraud
Tasks
- Collaborate with product engineering and risk leadership on priorities
- Conduct model training and experimentation
- Deploy models to production and monitor performance
- Design fraud detection and identity verification models lifecycle
- Drive AI usage in team workflows and define AI product opportunities
- Lead feature engineering and labeling strategy
- Manage applied machine learning scientists
- Own applied machine learning roadmap and execution
- Research emerging fraud patterns and build new ML capabilities
- Review pull requests and guide modeling decisions
- Set engineering and modeling practices
Perks/Benefits
- 401k match
- Company in person events
- Employer-paid health insurance
- Flexible paid time off
- Home office stipend
Skills/Tech-stack
Amazon Web Services | Applied statistics | Data Governance | Elasticsearch | Experimentation | Flyte | Fraud Detection | Identity verification | Language Models | Large Language Models | MLflow | Machine Learning | Monitoring | Neo4j | OpenSearch | Pandas | PostgreSQL | Production Machine Learning | Python | Scikit-learn | Web Services | XGBoost
Education
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