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

Originally posted on Himalayas