SentiLink

SentiLink stops more than 150,000 fraud attempts and verifies more than 3 million identities every day to protect both institutions and consumers. The problems we work on are challenging, and solving them takes deep domain expertise, rigorous analysis, and a willingness to dig into the details.
At SentiLink, you'll work alongside smart, highly collaborative colleagues who respect each other's time and judgment, take ownership, and follow through on their commitments. Together, we do truly meaningful work protecting the identities of innocent consumers and flagging the fraudsters attempting to exploit them.
We're well capitalized and growing quickly. We already serve 13 of the top 15 U.S. banks, we're expanding into new markets, and we're backed by Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin. We've been named to the Forbes Fintech 50 and as a 2026 FICO Industry Vanguard Decision Award Winner. We were also the first company to go live with the electronic Consent Based Social Security Number Verification (eCBSV) service, and we've testified before the U.S. Congress on the future of identity.
We want SentiLink to be the best place you've ever worked. We offer generous benefits and support a range of working arrangements, from fully remote to in-office. We are a digital-first company with strong collaboration across the U.S. and India, and we meet in person regularly to build relationships. We have 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.
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 (ML)

Head of Applied Machine Learning (ML)

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