Data Scientist, New Grad

Overview

Senti Link 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 quickly. Our real-time APIs have helped verify hundreds of millions of identities, starting with financial services and expanding into new markets.

Senti Link is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin. We’ve earned recognition from Tech Crunch, CNBC, Bloomberg, Forbes, Business Insider, PYMNTS, American Banker, Lend It, and have been named to the Forbes Fintech 50 list every year since 2023. 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.

Senti Link 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, 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.

As a Data Scientist at Senti Link, you will build our core products: models that identify fraudsters and 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 will 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  you’re looking for real-world AI and ML exposure in an industry setting, not just research papers, this is it.

• 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.

Python 3, Postgre

SQL, and AWS infrastructure (EC2, S3, RDS, Redshift, etc.).

• Develop and maintain Senti Link’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 Senti Link’s expanding suite of Fraud and Financial Risk products.
• Research new types of fraud and develop new Senti Link products around identity verification.
• Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.
• Write production-ready…