Applied ML Manager

Role:

SentiLink builds the fraud detection and identity verification models much of the US financial system runs on. As an Applied ML Manager, you own a core area 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: you'll coach, review code, and contribute directly where it matters most.

Data science drives product decisions here, and the growth path from this role is strategic leadership 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.

Technologies: Python 3, PostgreSQL, AWS, XGBoost, scikit-learn, pandas, Elasticsearch and OpenSearch, Neo4j, MLflow, Flyte, and use of modern LLM tooling.

Responsibilities:

• Directly manage a team of applied ML scientists, 4 today and growing to 6 by the end of 2026, and help set the engineering and modeling practices they work by.
• Own execution for your area: priorities, delivery, and the results your team produces.
• Coach and mentor team members through model development, experimentation, and technical decision making, and remain hands-on where it matters most, including code review and production systems.
• Partner with senior leadership, Product, Engineering, and Risk to prioritize work and deliver impactful ML solutions.
• Communicate progress, tradeoffs, and results clearly, and grow into representing your domain in product strategy discussions.
• Develop and improve 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.
• Use AI throughout your team's work, help push the boundary on what that unlocks, and contribute to our exploration of where AI belongs inside the products themselves.

Requirements:

• 6+ years of industry experience applying machine learning or statistics to real-world problems, including 3+ years directly managing machine learning or data science teams. Experience at high-growth startups preferred.
• Experience leading ML or data science teams in fraud, identity, fintech, banking, financial services, payments, or adjacent risk-focused domains, and interest in growing into ownership of product direction for that domain.
• 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.
• Comfortable working end to end, with a track record of 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.
• Comfortable with modern LLMs and AI-assisted development workflows and interested in pushing them further. Sound judgment when working with sensitive data under real information security and data governance constraints.
• Strong communicator who enjoys mentoring others while remaining technically hands-on. Detail oriented and thoughtful, someone we can rely on to make high-stakes calls 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.