Head of Applied Machine Learning – Application Fraud

This is a fully remote position, open to applicants in United States.

📋 Description

• Directly oversee a team of applied ML scientists, expanding from 4 to 6 members by the end of 2026.

• Establish the engineering and modeling standards utilized by the team.

• Take ownership of the strategy and execution within the applied ML domain, encompassing roadmap development, prioritization, resource allocation, and outcome assessment.

• Provide technical mentorship, guide modeling and architectural decisions, review pull requests, and maintain familiarity with the codebase and production systems.

• Collaborate with senior leadership, Product, Engineering, and Risk teams to set priorities and meet ambitious deadlines.

• Represent the domain in product strategy discussions and contribute to shaping the product's direction.

• Manage fraud detection and identity models through all stages including data acquisition, feature engineering, labeling, model training, experimentation, deployment, monitoring, and iteration.

• Investigate emerging fraud patterns and develop ML capabilities for identity verification and financial risk assessment.

• Design analyses that inform product and business decisions.

• Promote AI utilization in the team’s initiatives and assist in defining the role of AI in products.

⛳️ Requirements

• A minimum of 10 years of industry experience applying machine learning or statistics to real-world challenges, or 7 years with a relevant PhD.

• At least 6 years of direct experience managing machine learning or data science teams across two or more companies.

• Previous startup experience is highly preferred.

• Experience in leading ML or data science teams within fraud, identity, fintech, banking, financial services, payments, or related risk-focused sectors is strongly desired, though not strictly required.

• Bachelor's, Master's, or PhD in Computer Science, Statistics, Mathematics, Physics, or another quantitative field.

• Proven success in developing and deploying production machine learning models.

• Proficient in writing production-quality Python code and tests.

• Strong practical knowledge in ML and applied statistics.

• A history of owning a technical domain and driving measurable business impact.

• Familiarity with modern LLMs and AI-assisted development workflows.

• Sound judgment when handling sensitive data under real information security and data governance constraints.

• Excellent communication skills with senior leadership and cross-functional stakeholders.

• Detail-oriented and considerate.

• Legally authorized to work in the United States.

• Must reside in the United States.

• Technologies: Python 3, PostgreSQL, AWS, XGBoost, scikit-learn, pandas, Elasticsearch, OpenSearch, Neo4j, MLflow, Flyte, and modern LLM tools.

🏝️ Benefits

• Employer-funded group health insurance for you and your dependents.

• 401(k) plan with employer matching (or equivalent for roles based outside the US).

• Flexible paid time off.

• Regular company-wide in-person events.

• Home office stipend.

• Equity.