About 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, growing quickly with real-time APIs verifying hundreds of millions of identities, starting with financial services and expanding into new markets. SentiLink is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin. We operate as a digital-first company with strong collaboration across the U.S. and India, maintaining physical offices in Austin, San Francisco, New York City, Seattle, Los Angeles, Chicago, Gurugram (Delhi), and Bengaluru.
About the Role
As a Data Scientist at SentiLink, 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'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. 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.
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
Technologies Used:
- Python 3
- PostgreSQL
- AWS infrastructure (EC2, S3, RDS, Redshift, etc.)
Responsibilities
- 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.
Requirements
- Bachelor’s, Master’s, or 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.