Head of Machine Learning - Application Fraud

Role Details

• Role: Head of ML, similar to a Senior Data Science Manager.

• Responsible for managing the application fraud team, building zero to one models, and running the suite of fraud products.

Candidate Requirements

• Looking for top 1% individuals with a combination of data science and MLE skills.

• Preferred experience in cybersecurity, health tech, or fintech with a strong understanding of fraud models.

Technical and Experience Requirements

• Must have 4-5 years of management experience and have scaled products and teams.

• Needs to have hands-on coding ability, though they won’t code regularly, and must pass a technical coding exercise.

Education Requirements

• Preferred Master's or PhD in a relevant STEM field, but exceptional Bachelor's profiles considered.

Compensation and Logistics

• Salary range: $210k to $250k.

• Remote role in the US, with visa sponsorship available in most cases.

Interview Process

• Four rounds: recruiter call, hiring manager interview, technical (coding and case study), and leadership round.

• Leadership round involves the head of people, CTO, and CEO.

Ideal Candidate Profile

• Combination of machine learning engineering and data science, capable of writing production code.

• Strong communicator, able to articulate complex information effectively.

Team Culture and Expectations

• High-caliber, select team with high expectations and visibility.

• Autonomy in growing products and scaling teams, with a strong focus on technical and problem-solving capabilities.

Seniority

• 7 -​ 15 years of experience in applied ML/data science,​ building production models in fintech,​ cybersecurity,​ or other high stakes domains

• Work experience

• 4+ years managing data science/ML teams in high-​growth startups (must have managed people building and deploying ML products core to the business)

• Proven track record of solving complex / high profile business problems with DS / ML solutions.​ (Scaled a product suite of ML models,​ not just one model)

• Held senior/leadership role at a fast growing startup (20-​400 people) with broad scope

• Shows great slope and career progression

Education

Master's or PhD in a STEM field (math,​ stats,​ CS,​ physics,​ engineering) -​ target top universities

Hard skills

End-​to-​end ML:​ feature engineering,​ model training,​ productionalization,​ monitoring

Strong software engineering abilities in Python

Domain experience in fraud,​ identity verification,​ or financial risk

Soft skills

Experience in communicating progress + outcomes to senior management / stakeholders

Traits to avoid

• Background focused on LLMs,​ gen AI,​ RAG,​ or agentic AI — not what we're looking for in this hire

• Product/business analytics or experimentation background data scientists

• Only large-​company experience with narrow scope (e.​g.​,​ manager at Meta/Google with no product ownership)