- San Francisco, CA, United States
As a machine learning engineer at SentiLink, you will be responsible for developing the models that identify fraudsters and disambiguate identity. In addition to developing SentiLink's core product, you will interface with teams across the business: prioritizing manual review for risk analysts, running pilots, providing analysis to support our business development team, and helping to productionalize these systems and ensure scalability.
- Build out SentiLink’s core product, developing models to detect fraud and disambiguate identities
- Write production code that can be relied on for real-time decision making by our partners including top banks
- Work with data engineering to access necessary data, maintain data quality, and support data access for other teams
- Develop queues of applications for the risk operations to review, and communicate with them to stay on top of fraud trends
- Monitor our models and fraud trends
You Should Have
- Experience with Python and Pandas
- Solid understanding of different machine learning models and their trade-offs
- Experience writing production code and tests
- A PhD or Master’s in a technical field plus work experience
- Strong product focus
- Experience in fintech/financial services/lending/insurance industries or identity solutions
- Experience with Spark or other distributed computing frameworks
- Data engineering experience (scaling databases, writing ETL pipelines)
- Strong SQL
SentiLink is building the modern identity bureau and bringing consumer identity into the 21st century. As the world shifts online, both individuals and institutions are increasingly exposed to new vectors of fraud. To tackle this complex and evolving problem, SentiLink verifies identities in real-time using proprietary clustering technology and the latest machine learning techniques. Our platform enables companies to provide their customers with a frictionless identity verification experience while stopping fraud before it occurs.
SentiLink is the leader in preventing synthetic fraud, an emerging fraud vector in which fraudsters open financial accounts under nonexistent people. Our partners include top-ten US banks, fintechs, and alternative lenders.
Follow-through | Whatever it takes | Deep understanding
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