Quantitative Researcher, PhD New Grad

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.

As a Quantitative Researcher at SentiLink, you will build our core products: models that identify fraudsters and also advance our growing suite of products in financial risk. This role is designed for new PhD graduates or early-career researchers interested in applying machine learning to real-world fraud detection. You'll build and ship machine learning models in a production environment, gaining hands-on experience across the full ML lifecycle, from research and development to deployment at scale. If you're looking for real-world AI and ML exposure in an industry setting, not just research papers, this is it.

We have open roles on multiple teams including:

• Emerging Products - focuses on 0-to-1 development of new offerings brought to market

• Application Fraud - analyzes the foundational elements of consumer financial applications to detect all forms of fraud

• Identity - resolves identities across massive, often conflicting data sources (both digital and physical) and generates risk models from limited information

You will be relied upon to be technically capable and the definitive owner of your respective domain. You will often work on projects with high visibility and impact that require deep domain understanding, critical thinking and strong technical abilities. You will work with teams across the company to research new types of fraud, develop new products, and provide analysis to drive sales and marketing. This is a full-stack data science role, involving model development, analysis, and writing production code. You should be interested in having end-to-end ownership and a fast-moving environment where deep domain understanding drives development and unusual insights drive our competitive advantage rather than optimization of new machine learning methodologies.

This role can be remote within the U.S., with a strong preference for candidates who can work from our Austin, San Francisco, or New York offices.

Technologies: Python 3, PostgreSQL, and AWS infrastructure (EC2, S3, RDS, Redshift, etc.)

• Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.

• Build foundational modeling to drive SentiLink’s expanding suite of Fraud and Financial Risk products.

• Research new types of fraud and develop new SentiLink products around identity verification.

• Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.

• Write production-ready code that can be relied on for real-time decision making by our partners.

• Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.

• Work with engineering, risk operations, and data acquisitions to access necessary data, maintain data quality, and support data access

• PhD in Statistics, Computer Science, Physics, Mathematics, or a related quantitative field or equivalent experience/research

• Strong foundation in machine learning, statistics, or applied data science

• Experience with Python and common data science tools through coursework, research, internships, or personal projects

• Demonstrated ability to analyze complex problems and build data-driven solutions

• Strong communication skills and ability to explain technical ideas clearly

• Interest in learning deeply about fraud, identity, and financial risk systems

• Ability to write clean, maintainable code

• Strong attention to detail and curiosity about real-world data problems

• Candidates must be legally authorized to work in the United States and must live in the United States

• Thrive in a fast paced environment characterized by the need to solve extremely varied, high impact, open ended problems

$120,000/year - $220,000/year + equity + benefits

Note: This salary range is inclusive of multiple career levels, and the actual base salary within that range will be determined by several components including but not limited to the individual's education, experience, skills, and qualifications.

• Employer paid group health insurance for you and your dependents

• 401(k) plan with employer match (or equivalent for non US-based roles)

• Flexible paid time off

• Regular company-wide in-person events

• Home office stipend, and more!

• Follow Through

• Deep Understanding

• Whatever It Takes

• Do Something Smart