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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.

• 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.

• 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).

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.

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

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.

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.

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.