Position : Head of Machine Learning - Application Fraud-Remote
Duration : Fulltime
Location : Remote
Interview Mode :
- 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.
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.
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
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7 - 15 years of experience in applied ML/data science, building production models in fintech, cybersecurity, or other high stakes domains
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Work experience
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4+ years managing data science/ML teams in high-growth startups (must have managed people building and deploying ML products core to the business)
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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)
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Held senior/leadership role at a fast growing startup (20-400 people) with broad scope
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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
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Background focused on LLMs, gen AI, RAG, or agentic AI — not what we're looking for in this hire
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Product/business analytics or experimentation background data scientists
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Only large-company experience with narrow scope (e.g., manager with no product ownership)
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