About the Role

We are seeking an exceptional Head of Machine Learning to lead our Application Fraud team and drive the development of next-generation machine learning models that power our fraud detection platform.

This is a highly visible leadership role responsible for managing a team of Machine Learning Engineers and Data Scientists while remaining technically hands-on. You'll own the strategy, development, deployment, and evolution of a suite of production machine learning models that solve complex fraud challenges at scale.

We're looking for a leader who combines deep technical expertise with strong people management skills and thrives in fast-paced, high-growth startup environments.

What You'll Do

• Lead and grow the Application Fraud Machine Learning team.
• Build, deploy, and scale production-grade machine learning models for fraud detection and risk assessment.
• Own the end-to-end lifecycle of multiple ML products, from feature engineering through production deployment and ongoing monitoring.
• Partner closely with engineering, product, and executive leadership to define technical strategy and business priorities.
• Mentor, coach, and develop high-performing Machine Learning Engineers and Data Scientists.
• Drive technical excellence across model development, deployment, experimentation, and performance optimization.
• Establish scalable processes for model monitoring, retraining, and continuous improvement.
• Translate complex technical concepts into clear business recommendations for executive stakeholders.
• Help shape the long-term vision and roadmap for the company's fraud detection platform.

Required Qualifications

• 7–15 years of experience in Applied Machine Learning or Data Science.
• 4+ years leading Machine Learning or Data Science teams in high-growth startups.
• Proven experience building and deploying production machine learning models that are core to a company's business.
• Demonstrated success scaling both ML products and technical teams.
• Experience leading multiple production models or an entire ML product suite—not just a single model.
• Strong career progression demonstrating increasing ownership and leadership.
• Previous leadership experience at a fast-growing startup (approximately 20–400 employees).

Technical Qualifications

Candidates should have expertise in:

• End-to-end Machine Learning lifecycle
• Feature engineering
• Model training and validation
• Production deployment (Productionalization)
• Model monitoring and optimization
• Python software development
• Production-quality software engineering
• Machine Learning infrastructure and scalable ML systems

Strong hands-on coding skills are required. While this role is primarily leadership-focused, candidates must be capable of contributing technically and successfully completing a live coding assessment.

Preferred Domain Experience

Strong preference for candidates with experience in:

• Application Fraud
• Fraud Detection
• Identity Verification
• Financial Risk
• FinTech
• Cybersecurity
• Healthcare Technology
• Other high-stakes machine learning domains

Education

Preferred qualifications include:

• Master's or Ph.D. in:
  • Computer Science
  • Statistics
  • Mathematics
  • Physics
  • Engineering
  • Related STEM discipline

Exceptional candidates with a Bachelor's degree and outstanding industry experience will also be considered.

What We're Looking For

The ideal candidate combines deep Machine Learning expertise with strong engineering fundamentals and proven leadership experience.

Successful candidates will demonstrate:

• Technical excellence in both Machine Learning Engineering and Data Science.
• Ability to write production-quality Python code.
• Strong problem-solving skills in complex, high-impact environments.
• Experience leading high-performing technical teams.
• Excellent communication skills with executive leadership and cross-functional stakeholders.
• Ability to independently drive product strategy and execution.
• A passion for mentoring engineers and scaling teams.

Candidates Unlikely to Be a Fit

The following backgrounds generally do not align with this opportunity:

• Machine Learning professionals focused primarily on LLMs, Generative AI, Retrieval-Augmented Generation (RAG), or Agentic AI.
• Data Scientists whose experience centers on product analytics, experimentation, or business intelligence rather than production machine learning.
• Leaders with only large enterprise or Big Tech experience and limited end-to-end product ownership.
• Candidates without hands-on production Machine Learning experience.
• Managers who have not built and scaled multiple production ML models or teams.