We Love AI JobsHead of Machine Learning
kadenceAs the Head of Machine Learning at Kadence, you'll lead a growing team of data scientists dedicated to developing fraud detection models within the fast-paced fintech industry. This role emphasizes hands-on leadership, collaboration across functions, and the opportunity to drive impactful solutions that enhance financial risk products.
Skills & Expertise
Key Responsibilities
Manage and grow a team of 2-6 data scientists.
Lead planning and cross-functional communication with key stakeholders.
Own the full lifecycle of fraud detection models from development to monitoring.
Full Description
Role: Head of Machine Learning, Application Fraud
As Head of Machine Learning, Application Fraud, you will lead and grow a team of high-performing data scientists building models that detect fraud and power a broader suite of financial risk products. This is a highly hands-on leadership role where you will serve as a technical leader, mentor, and domain owner.
You’ll be expected to go deep with your team, challenge their thinking, and guide them toward high-impact solutions. The work is fast-moving, highly visible, and requires strong domain intuition, critical thinking, and end-to-end ownership. The focus is less on novel ML techniques and more on applying strong fundamentals, deep problem understanding, and creative insights to drive real-world outcomes.
This team operates across the full stack of data science, including model development, analysis, and production-level code. You should be comfortable operating in that same capacity.
Tech Stack
Python (3), PostgreSQL, AWS (EC2, S3, RDS, Redshift)
What You’ll Do
* Manage and grow a team of data scientists (starting with 2–3, scaling to 5–6)
* Act as a hands-on technical mentor, providing detailed guidance and direction
* Lead planning, resourcing, and cross-functional communication with product, engineering, and leadership
* Develop strong business intuition and guide the team to deliver high-impact solutions under tight timelines
* Own the full lifecycle of fraud detection models: data sourcing, feature engineering, labeling strategy, training, experimentation, deployment, and monitoring
* Research emerging fraud patterns and contribute to new product development in identity and risk
* Drive innovation through iteration, new data sources, and creative feature engineering
* Write production-grade code used in real-time decision systems
* Design and present analyses that inform product, data strategy, operations, and go-to-market efforts
What We’re Looking For
* 10+ years of relevant experience with a Master’s, or 7+ years with a PhD
* 2–5 years managing data science teams, ideally in a startup environment
* 3+ years of startup experience
* Strong communicator and collaborative team player
* Proven track record of solving complex, high-impact business problems using data science and machine learning
* Experience presenting to and influencing senior stakeholders
* Deep experience across the full data science lifecycle, from problem framing through production delivery
* Strong practical ML and statistical foundation, with the ability to quickly scope and execute solutions
* Interest in developing deep domain expertise in a product-focused environment (fraud experience not required)
* Experience writing production-quality code and tests
* High attention to detail and sound decision-making judgment
* Comfortable operating in a fast-paced environment with ambiguous, high-impact problems
Nice to Have
* Experience in identity, fintech, or related domains
Benefits
* Employer-sponsored health insurance (including dependents)
* 401(k) with company match
* Flexible PTO
* Company-wide in-person events
* Home office stipend