Head of Machine Learning (Fraud & Risk) – Remote

About the Role We are seeking an exceptional Head of Machine Learning to lead our Fraud & Risk Machine Learning organization. This is a highly visible leadership role responsible for building and scaling the next generation of fraud detection and risk decisioning products. You'll lead a high-performing ML team while remaining technically credible, partnering closely with Product, Engineering, and Executive Leadership to develop production-grade machine learning systems that directly impact the business. This role is ideal for a hands-on technical leader who has successfully scaled ML products and teams in fast-growing startup environments. Location Remote (United States) Compensation $210,000 – $250,000 base salary Exceptional candidates may be considered up to $260,000 Competitive equity package Comprehensive benefits Visa sponsorship available for qualified candidates What You'll Do Lead the Fraud & Risk Machine Learning organization, managing a team responsible for production fraud detection models. Define and execute the machine learning roadmap for fraud prevention, identity verification, and risk decisioning. Build and scale a portfolio of production ML models from concept through deployment and continuous optimization. Partner with Product, Engineering, Risk, and Executive Leadership to solve complex business challenges using machine learning. Drive end-to-end machine learning development including: Feature engineering Data preparation Model development Validation Production deployment Monitoring and model performance optimization Establish best practices for model governance, experimentation, and production reliability. Mentor and grow a high-performing team of Data Scientists and Machine Learning Engineers. Provide technical leadership while remaining capable of contributing hands-on when necessary. Present technical strategy, business impact, and model performance to executive stakeholders. Required Qualifications 7–15 years of experience in Applied Machine Learning or Data Science. 4+ years leading and managing Machine Learning or Data Science teams. Proven success building and scaling production machine learning products in high-growth startup environments. Experience leading teams responsible for ML systems that are core to the business. Strong software engineering skills with production-level Python development. Deep experience across the full machine learning lifecycle: Feature engineering Model training Model evaluation Production deployment Monitoring Continuous improvement Domain expertise in one or more of the following: Fraud Detection Financial Risk Identity Verification Cybersecurity Experience owning multiple production ML models rather than a single isolated project. Strong leadership, communication, and stakeholder management skills. Ability to communicate technical concepts clearly to executives and cross-functional partners. Preferred Qualifications Experience at high-growth startups (approximately 20–400 employees). Track record of scaling both machine learning products and engineering organizations. Experience solving complex, high-impact business problems through machine learning. Strong business acumen with the ability to align ML strategy to company objectives. Demonstrated career progression into increasingly broader technical leadership roles. Education Master's or PhD in Computer Science, Statistics, Mathematics, Physics, Engineering, or another STEM discipline preferred. Exceptional candidates with a Bachelor's degree and outstanding industry experience will also be considered. Ideal Candidate We're looking for someone who: Combines deep machine learning expertise with strong software engineering fundamentals. Has built and deployed production ML systems at scale. Can balance strategic leadership with technical depth. Enjoys mentoring and developing high-performing teams. Thrives in fast-paced startup environments. Takes ownership of business outcomes—not just model accuracy. Is comfortable influencing technical direction and executive decision-making. Technical Skills Python Machine Learning Feature Engineering Model Training & Evaluation Model Deployment & Monitoring Fraud Detection Identity Verification Financial Risk Modeling Production ML Systems Data Science Software Engineering