⚡ Head of Machine Learning, Fraud

🌍 US Remote

💲 $230,000–$260,000/year base + equity + benefits

The Company

Storm2’s client is an NYC-based fintech rebuilding the identity verification infrastructure underneath US financial services. Most of it is fragmented and years out of date, and most of the industry has quietly accepted it as good enough. Their real-time APIs have already run across hundreds of millions of identities, and their fraud models make decisions their partners depend on live. They’ve raised $60M+ from 3x Tier 1 VCs, and they’ve been named among the sector’s standout companies.

The Role

The interesting part of this job isn’t the ML methodology. It’s the domain, the data, and the unusual insights that come from understanding fraud better than anyone else. This team’s edge comes from deep domain knowledge, not from optimizing the latest modelling technique.

You’d directly manage a team of full-stack data scientists building the models that catch fraudsters, plus a growing suite of products in financial risk. Full-stack here is literal: your team owns model development, analysis, and the production code that runs those decisions in real time. You’re expected to share that skillset, not sit above it. You’ll get into the models, challenge your team’s thinking, and own your domain end to end.

The team is small today, two or three people, and you’d grow it to five or six. The work is high visibility and high stakes, the timelines are aggressive, and the person who does well here pairs strong business intuition with real technical depth. This is a build role, not a maintenance one.

Worth saying plainly: this is applied fraud ML with real money on the line, not an LLM, RAG, or agentic AI role. If your last few years have been about shipping fraud or risk models that measurably move the business, this will feel like home.

What you’ll be working on:

• Managing a team of data scientists, growing it from two or three to five or six, and mentoring them technically rather than just managing process
• Owning the full model development lifecycle: data acquisition, featurization, labelling strategy, training, experimentation, productionalization, and monitoring
• Writing production-ready Python that partners rely on for real-time decisions
• Researching new fraud typologies and building new identity verification products around them
• Leading planning, resourcing, and communication with senior leadership, product, and engineering
• Designing and presenting analyses that steer data acquisition, product, risk ops, marketing, and sales

What you’ll bring:

• 7 to 15 years in applied ML or data science, building production models in fintech, cybersecurity, or another high-stakes domain
• 4+ years managing DS/ML teams at high-growth startups, where your people were shipping ML products core to the business
• A track record scaling a suite of ML models, not one, against complex, high-profile problems
• A senior or leadership role at a fast-growing startup (roughly 20 to 500 people) with genuinely broad scope, and a career that shows real slope
• Strong software engineering in Python, and comfort staying hands-on
• Domain experience in fraud, identity verification, or financial risk
• An MS or PhD in a STEM field (maths, stats, CS, physics, engineering), ideally from a top program
• The communication to keep senior stakeholders close to what your team is finding

Tech stack

Python 3, PostgreSQL, AWS (EC2, S3, RDS, Redshift), and the usual ML tooling.

Who this isn’t for

If your background is centred on LLMs, gen AI, RAG, or agents, this isn’t that hire. Same if you’re coming from product/business analytics or experimentation-heavy data science, or from a large company where the title was senior but the scope was narrow. None of that is a knock, it’s just not this seat, and knowing it up front saves everyone time.

📧 Click ‘Easy Apply’ or email ben.watts@storm2.com

⚡ Storm2 is a specialist FinTech recruitment firm with clients across North America. Visit storm2.com or follow us on LinkedIn for the latest roles and intel.

• Seniority Level

  Director

• Industry

  • Financial Services
  • Software Development

• Employment Type

  Full-time

• Job Functions

  • Engineering

• Skills

  • Sales
  • Financial Risk Management
  • Fraud Investigations
  • Physics
  • Ops
  • Amazon Redshift
  • Presentations
  • Amazon Web Services (AWS)
  • T