Head of Applied Machine Learning

Role:

SentiLink builds the fraud detection and identity verification models much of the US financial system runs on. As Head of Applied ML, you own a major ML domain end to end.

You'll lead a team of 4 applied ML scientists, 6 by the end of 2026, all experienced and technically deep enough to challenge you daily. This is a people management role that stays close to the work. Technical credibility is non-negotiable: you'll review PRs, push on modeling decisions, and unblock the team.

Data science drives product decisions here, and we expect you to become a strategic leader in both the product and ML domains you own. We use AI across all of our work, are exploring where it belongs in the products themselves, and hold a hard line on AI safety and data governance.

Responsibilities:

• Directly manage a team of applied ML scientists, 4 today and growing to 6 by the end of 2026, and set the engineering and modeling practices they work by.
• Own strategy and execution for your applied ML domain: roadmap, priorities, resourcing, and results.
• Act as a technical mentor who can dive deep and give specific, useful direction. Guide modeling and architecture decisions, review PRs, and stay current on the codebase and production systems.
• Partner with senior leadership, Product, Engineering, and Risk to set priorities and deliver on aggressive timelines.
• Represent your domain in product strategy discussions and help shape where those products go next.
• Own SentiLink's fraud detection and identity models across the full lifecycle: data acquisition, feature engineering, labeling strategy, model training, experimentation, production deployment, monitoring, and iteration.
• Research emerging fraud patterns, build new ML capabilities for identity verification and financial risk, and design analyses that inform product and business decisions.
• Drive how the team uses AI in its own work, keep pushing the boundary on what that unlocks, and help define where AI belongs in our products.

Requirements:

• 10+ years of industry experience applying machine learning or statistics to real-world problems, or 7+ years with a relevant PhD, including 6+ years directly managing machine learning or data science teams across two companies or more. Startup experience strongly preferred.
• Experience leading ML or data science teams in fraud, identity, fintech, banking, financial services, payments, or adjacent risk-focused domains. Strongly desired, but not strictly required.
• Bachelor's, Master's, or PhD in Computer Science, Statistics, Mathematics, Physics, or another quantitative discipline.
• Demonstrated success developing and deploying production machine learning models, plus experience writing production-quality Python code and tests.
• Strong practical ML and applied statistics knowledge: able to scope solutions quickly with standard tooling and go deep where it pays off.
• Very strong end to end, with a track record of owning a technical domain and driving it to measurable business impact: planning, defining success criteria, getting buy-in, building the solution, and delivering it, whether in production, in a deck, or as strategy.
• Fluent with modern LLMs and AI-assisted development workflows, and opinionated about where they help and where they don't. Sound judgment when working with sensitive data under real information security and data governance constraints.
• Excellent communicator, including with senior leadership and cross-functional stakeholders. Detail oriented and thoughtful, someone we can rely on to make business-changing decisions while thriving on varied, open-ended, high-impact problems.
• Candidates must be legally authorized to work in the United States and must live in the United States.