SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
SentiLink is a Series B fintech company providing identity verification and risk solutions to financial institutions. The company has achieved significant traction with APIs verifying hundreds of millions of identities, backed by top-tier investors (Craft Ventures, Andreessen Horowitz, NYCA, Max Levchin), and recognized by Forbes as a Fintech 50 company.
You will lead SentiLink's Model Risk and Governance function end-to-end, managing a team of 3+ data scientists and governance professionals. This is a hands-on leadership role where you'll set long-term strategy while remaining deeply involved in execution.
Key responsibilities include: leading and developing your team with room to scale; owning core governance fundamentals (performance monitoring, drift detection, fair lending assessments, documentation, validation, model inventory, change management); setting strategy on what to automate, standardize, and where to exceed industry norms; managing cross-functional relationships with Data Science, Engineering, Partner Success, and Sales; owning direct customer relationships with bank and fintech model risk teams; preparing validation reports and governance documentation for leadership, customers, auditors, and regulators; tracking governance findings through remediation; and balancing strategic work against customer and regulatory demands.
This role is critical to company growth—governance gates how quickly customers can adopt SentiLink's models, making it a direct lever on revenue and expansion.
Required qualifications: 8+ years in model risk management, validation, governance, or quantitative risk with proven experience building or scaling governance/risk teams; 4+ years managing people with demonstrated success building and scaling model risk or governance teams; deep knowledge of financial institution model governance (SR 11-7, SR 26-2, OCC guidance, fair lending, regulatory landscape) with firsthand experience validating ML/statistical models in regulated environments; genuine technical depth to read models, interrogate methodology, and engage with data scientists; working knowledge of Python and SQL proficiency; strong bias for action balancing governance rigor with speed; excellent analytical and communication skills (Excel/Google Sheets, technical and non-technical audiences); Bachelor's degree in quantitative field (Math, Statistics, CS, Engineering, Economics, or related STEM); legal authorization to work and reside in the US.
Nice-to-haves: experience with fraud, identity verification, credit risk, or financial risk models; experience supporting model go-live processes.
