Partner with Machine Learning teams, GenAI application developers, business sponsors, and other stakeholders to maintain accurate inventories, risk assessments, documentation, monitoring reports, and supporting governance materials
Review methodologies, assumptions, data inputs, system designs, performance measures, controls, and limitations to provide effective challenge
Apply risk-based approach to evaluate quantitative methods and technologies from traditional statistical models to complex machine learning and GenAI applications
Conduct and document model risk assessments, monitoring reviews, and targeted quantitative analyses
Help develop practical governance approaches for new and rapidly evolving technologies
Respond to model- and GenAI-related questions from regulators, lending partners, and external stakeholders
Track model risk issues, remediation plans, program goals, and emerging risks

Master's degree in quantitative field such as finance, mathematics, economics, statistics or related discipline
4+ years of experience in model risk management, model validation, model governance, machine learning, data science, quantitative risk, AI governance, or related technical risk function
Internship or project experience related to model risk management, model validation, machine learning, or data science
Basic understanding of AI/ML methodologies such as tree-based models and neural networks
General familiarity with GenAI applications
Experience coding in R, Python, or similar languages such as Matlab

PhD in quantitative field such as statistics, econometrics, finance, mathematics; or related discipline
5+ years of experience in model risk management or model governance, or related fields such as ML and Data Science, Risk, Trust and Safety, or Technical Writing
Familiarity with GenAI applications, including evaluation approaches, prompt and system design, retrieval-augmented generation, tool use, guardrails, and ongoing monitoring
Experience assessing models used outside of credit underwriting, such as models supporting fraud, compliance, finance, capital and liquidity, servicing, operational risk, or financial reporting
Strong communication skills: ability to adapt technical information to varying needs and audiences
Proactive mindset with ability to take initiative
Understanding of advanced AI/ML topics such as model monitoring, fairness, and explainability
Advanced coding skills in R, Python, and SQL, and experience using Git
Interest in or knowledge of consumer lending, credit risk, model fairness, explainability, or use of machine learning and GenAI in regulated environment

Competitive compensation including base pay, bonus opportunities, and annual equity grants
401(k) or Group Retirement Savings Plan with company match of $2 for every $1 contributed up to $15,000 annually
Employee Stock Purchase Plan with discounted stock purchase options
Comprehensive health coverage including medical, dental, vision, and wellness resources
Health Savings Account contributions from Upstart
Life insurance and disability coverage
Paid time off, sick leave, and company holidays
Paid family and parental leave
Family-centered benefits to support fertility, parenthood, and caregiving
Employee Assistance Program offering mental health support
Financial wellness resources including financial planning tools and financial concierge service
Annual wellness allowance
Annual productivity allowance
Team events, company updates, and employee resource groups
Onsite perks including catered lunches and fully stocked micro-kitchens

Hybrid — Remote, Bay Area, Austin, Columbus, New York City

Travel requirements: Majority of work accomplished remotely with regular onsites 1-2 times per quarter for 2-4 consecutive days
Position available remotely in US
Not currently able to hire in Quebec for Canada-based roles

Upstart is hiring a Staff Machine Learning Model Risk Specialist