Quantitative Scientist, PhD New Grad

As a Quantitative Scientist 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.)