# Manager, Quantitative Risk Management at SentiLink

- Company: SentiLink
- What the company does: SentiLink combines technology and expertise to help financial institutions stop identity fraud at the application stage. Backed by a16z and Felicis.
- Company website: https://www.sentilink.com
- Type: Startups
- Level: Senior
- Location: United States
- Work setup: Remote
- Pay: $210K to $240K base salary per year (USD)
- Posted: 2026-08-13
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/sentilink/375d0414-3351-4eea-9b84-9d4e9c8f402b
- Page: https://www.1752.vc/careers/jobs/sentilink-manager-quantitative-risk-management/

## About the role

Lead, grow, and develop a team of 3+, with real room to scale as the business does. Own the fundamentals and raise the bar on how we execute them: performance monitoring, drift monitoring, fair lending assessments, governance documentation, validation, model inventory, and change management.

## What they're looking for

- 8+ years in model risk management, model validation, model governance, or quantitative risk, including proven experience building or scaling a governance/risk team (not just operating within one)
- 4+ years of people management experience with proven experience building and scaling model risk or governance teams, not just operating within one
- Deep knowledge of model governance for financial institutions. You know SR 11-7, SR 26-2, OCC guidance, fair lending, and the regulatory landscape, and you have firsthand experience validating or governing ML/statistical models in a regulated environment
- Genuine technical depth: able to read the model, interrogate the methodology, and hold your own with data scientists. Working knowledge of Python and proficiency in SQL
- A strong bias for action. You balance governance rigor against speed with judgment rather than defaulting to either
- Strong analytical skills (Excel/Google Sheets) and excellent written/verbal communication, comfortable translating technical findings for both technical and non-technical audiences

Tags: Data Science