Head of Machine Learning - Application Fraud

Location: San Francisco, CA / New York, NY / Los Angeles, CA / Boston, MA / Seattle, WA / Chicago, IL / South Bay Area / Texas / Washington DC / Denver / Florida (remote-friendly)

Salary: $230K - $260K + Competitive equity

Industry: Fintech / Fraud Detection

Who you'll join

A Series B fintech building fraud detection models that financial institutions rely on for real-time decisions. Their data science team is among the strongest in the industry, with acceptance rates well under 1% of candidates screened. The fraud detection products they build are core to the business, not a side function, and the problems never stand still.

What you'll do

1. Manage and grow a team of 2-3 data scientists to 5-6.
2. Mentor technically by diving deep into your team's work.
3. Lead planning and communicate priorities across product, engineering, and leadership.
4. Own the full model development lifecycle from data acquisition through production monitoring.
5. Research emerging fraud patterns and translate findings into new product directions.
6. Write production-ready code used in real-time partner decision-making.
7. Design and present analyses that inform product, risk, marketing, and sales teams.

Who you are

1. Built and deployed models that were core to a prior company's business.
2. Led a data science or applied ML team for at least 4 years.
3. Comfortable writing production code and passing a rigorous technical assessment.
4. Comes from fintech, cybersecurity, health tech, or insurance, not pure analytics.
5. Thinks from first principles about ambiguous problems and drives them to deployment.
6. Articulates complex technical work clearly to senior non-technical stakeholders.
7. Bonus: Experience with identity verification, graph databases, or feature stores.

Tech stack

Python 3, PostgreSQL, AWS (EC2, S3, RDS, Redshift), ML Tooling, real-time APIs, graph databases, feature stores, Kubernetes, experimentation platforms, identity verification systems

Why you'll thrive

1. Own the product suite and team direction with full autonomy from day one.
2. Work on fraud problems that are genuinely unsolved and constantly evolving.
3. Join a team where data science drives the company, not supports it.
4. Receive equity in a company at a stage where that equity still matters.
5. Build alongside a team consistently described as one of the strongest in the field.