ashby
Head of Applied Machine Learning - Application Fraud
SentiLink
Remote, USA
Full-time
Senior · 10+ years experience
Remote
USD 210000-260000 yearly / year
Discovered 2 weeks ago
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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.
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 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.
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