# Head of Applied Machine Learning (ML) at SentiLink

- Company: SentiLink
- What the company does: SentiLink combines technology and expertise to help financial institutions stop identity fraud at the application stage. Backed by a16z and Felicis.
- Company website: https://www.sentilink.com
- Type: Startups (AI role)
- Level: Principal and up
- Location: United States
- Work setup: Remote
- Pay: $210K to $260K base salary per year (USD)
- Posted: 2026-08-21
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/sentilink/ba414a52-687a-45ad-9e2c-eae5b977cfaf
- Page: https://www.1752.vc/careers/jobs/sentilink-head-of-applied-machine-learning-ml/

## About the role

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.

## What they're looking for

- 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
- 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

Tags: Data Science