Manager, Model Risk and Governance

What You'll Do:
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.

Set the strategy for the function. Decide what we automate, what we standardize, and where we need to be better than the industry norm.

Own relationships for anything governance-related, across Data Science, Engineering, Partner Success, and Sales.

Own customer relationships directly. Run customer-facing calls, work with model risk teams at banks and fintechs, and get ahead of the relationships that matter most.

Guide customers on pushing adoption forward while meeting their governance standards. You'll often be the one who unblocks a deal or a deployment.

Prepare validation reports, governance documentation, and performance summaries for internal leadership, customers, auditors, and regulators

Track governance findings through remediation and manage the team's roadmap, balancing strategic work against customer and regulatory demands

Do the work yourself when it's warranted, to move something forward or to mentor the team.

This is a high-leverage role. Governance gates how quickly our customers can adopt what we build, which makes it a direct lever on the company's growth, with substantial room for the right person to define it and grow with it.

What We'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

Bachelor's degree in a quantitative field (Math, Statistics, CS, Engineering, Economics, or related STEM)

Must be legally authorized to work in and reside in the US

Nice to haves:
Experience working with fraud, identity verification, credit risk, or financial risk models

Experience supporting model governance with banks or regulated financial institutions

Experience with AWS (S3, SageMaker) and GitHub

Master's degree in a quantitative field

Compensation:
$210,000-$240,000/year + equity + benefits

Perks:
Employer paid group health insurance for you and your dependents

401(k) plan with employer match (or equivalent for non US-based roles)

Flexible paid time off

Regular company-wide in-person events

Home office stipend, and more!

Corporate Values:
Follow Through

Deep Understanding

Whatever It Takes

Do Something Smart

Skills Required
8+ years in model risk management, model validation, model governance, or quantitative risk, including proven experience building or scaling a governance/risk team
4+ years of people management experience with proven experience building and scaling model risk or governance teams
Deep knowledge of model governance for financial institutions (SR 11-7, SR 26-2, OCC guidance, fair lending) and firsthand experience validating or governing ML/statistical models in a regulated environment
Working knowledge of Python
Proficiency in SQL
Strong analytical skills (Excel/Google Sheets) and excellent written/verbal communication
Bachelor's degree in a quantitative field (Math, Statistics, CS, Engineering, Economics, or related STEM)
Must be legally authorized to work in and reside in the US
Experience working with fraud, identity verification, credit risk, or financial risk models
Experience supporting model governance with banks or regulated financial institutions
Experience with AWS (S3, SageMaker) and GitHub
Master's degree in a quantitative field