SentiLink

SentiLink provides innovative identity and risk solutions, empowering institutions and individuals to transact with confidence. We’re building the future of identity verification in the United States replacing a clunky, ineffective, and expensive status quo with solutions that are 10x faster, smarter, and more accurate.
We’ve seen tremendous traction and are growing extremely quickly. Our real-time APIs have helped verify hundreds of millions of identities, starting with financial services and rapidly expanding into new markets. SentiLink is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.
We’ve earned recognition from TechCrunch, CNBC, Bloomberg, Forbes, Business Insider, PYMNTS, American Banker, LendIt, and have been named to the Forbes Fintech 50. We have also been named a 2026 FICO Industry Vanguard Decision Award Winner. Last but not least, we’ve even made history - we were the first company to go live with the eCBSV and testified before the United States House of Representatives on the future of identity.
SentiLink supports a variety of ways to work, ranging from fully remote to in-office. We operate as a digital-first company with strong collaboration across the U.S. and India. We maintain physical offices in Austin, San Francisco, New York City, Seattle (Bellevue), Los Angeles, and Chicago in the U.S., and in Gurugram (Delhi) and Bengaluru in India. If you’re located near one of these offices, we would love for you to spend time in the office regularly. Some roles are hybrid or in-office by design. For example, our engineering team in India works primarily from our Gurugram office.
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
<ul>
<li>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.</li>
<li>Own strategy and execution for your applied ML domain: roadmap, priorities, resourcing, and results.</li>
<li>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.</li>
<li>Partner with senior leadership, Product, Engineering, and Risk to set priorities and deliver on aggressive timelines.</li>
<li>Represent your domain in product strategy discussions and help shape where those products go next.</li>
<li>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.</li>
<li>Research emerging fraud patterns, build new ML capabilities for identity verification and financial risk, and design analyses that inform product and business decisions.</li>
<li>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.</li>
</ul>
Requirements
<ul>
<li>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.</li>
<li>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.</li>
<li>Bachelor's, Master's, or PhD in Computer Science, Statistics, Mathematics, Physics, or another quantitative discipline.</li>
<li>Demonstrated success developing and deploying production machine learning models, plus experience writing production-quality Python code and tests.</li>
<li>Strong practical ML and applied statistics knowledge: able to scope solutions quickly with standard tooling and go deep where it pays off.</li>
<li>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.</li>
<li>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.</li>
<li>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.</li>
<li>Candidates must be legally authorized to work in the United States and must live in the United States.</li>
</ul>
Compensation
$210,000-$260,000/year + equity + benefits
Perks
<ul>
<li>Employer paid group health insurance for you and your dependents</li>
<li>401(k) plan with employer match (or equivalent for non US-based roles)</li>
<li>Flexible paid time off</li>
<li>Regular company-wide in-person events</li>
<li>Home office stipend, and more!</li>
</ul>
Corporate Values
<ul>
<li>Follow Through</li>
<li>Deep Understanding</li>
<li>Whatever It Takes</li>
<li>Do Something Smart</li>
</ul>

Head of Applied Machine Learning - Application Fraud

Head of Applied Machine Learning - Application Fraud

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