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220–280 k $US par an · Télétravail · États-Unis
SentiLink
The threat model at SentiLink is application-stage identity fraud - synthetic identities, stolen credentials, first-party schemes - hitting financial institutions before a single dollar moves. Founded in 2017, the company has built a real-time identity verification platform that scores and flags fraudulent applications at onboarding, processing hundreds of millions of identities for banks, fintechs, and financial services firms across the United States. The technical stack centers on machine learning and risk analytics , with models trained on large-scale application data to catch both established and emerging fraud patterns. Human risk analysts work alongside the ML pipeline - this isn't pure-automation territory. The platform has earned SentiLink repeated placement on the Forbes Fintech 50 list. Engineering and security teams here are working at the intersection of adversarial machine learning and financial crime. Fraud actors constantly evolve their techniques; the defensive models need to keep pace. If you're building detection systems where precision and recall have direct dollar consequences, SentiLink operates squarely in that domain.
The threat model at SentiLink is application-stage identity fraud - synthetic identities, stolen credentials, first-party schemes - hitting financial institutions before a single dollar moves. Founded in 2017, the company has built a real-time identity verification platform that scores and flags fraudulent applications at onboarding, processing hundreds of millions of identities for banks, fintechs, and financial services firms across the United States.
The technical stack centers on machine learning and risk analytics, with models trained on large-scale application data to catch both established and emerging fraud patterns. Human risk analysts work alongside the ML pipeline - this isn't pure-automation territory. The platform has earned SentiLink repeated placement on the Forbes Fintech 50 list.
Engineering and security teams here are working at the intersection of adversarial machine learning and financial crime. Fraud actors constantly evolve their techniques; the defensive models need to keep pace. If you're building detection systems where precision and recall have direct dollar consequences, SentiLink operates squarely in that domain.