This role pays less than
79%
of similar roles. Most pay
$196,925–$259,212
— the shaded band above.
At the midpoint, this role pays about
$187k
versus about
$228k
for comparable roles.
Based on 239 similar postings.
Employer
About Upstart
Upstart is an AI lending platform that partners with banks and credit unions to expand access to affordable credit using non-traditional variables.
Upstart currently has
73 open roles
on FindRole.
Listed pay typically runs
$166,900–$230,450
across 72 roles with salary data.
Staff Machine Learning Model Risk Specialist joins the Model Risk team to manage and mitigate risks associated with models impacting the new Upstart Bank. This role involves overseeing a diverse inventory of machine learning models and Generative AI applications used for lending, fraud, compliance, finance, capital, liquidity, and operational risk. You will evaluate documentation, monitoring, governance, and risk assessments while partnering with developers and stakeholders to identify emerging risks. The position requires evaluating methodologies, data inputs, and system designs across traditional statistical methods and advanced machine learning systems. Candidates must possess a master's degree in a quantitative field and experience in model risk management or data science. Required skills include proficiency in R, Python, or Matlab, along with an understanding of tree-based models, neural networks, and GenAI applications like retrieval-augmented generation and prompt design.
Oversee risk across a diverse inventory of machine learning models and Generative AI applications for Upstart Bank.
Evaluate model documentation, monitoring, governance, and risk assessments to identify areas requiring remediation.
Review methodologies, data inputs, and system designs to provide effective challenge and technical oversight.
Conduct and document model risk assessments and targeted quantitative analyses to meet regulatory expectations.
Develop practical governance approaches for rapidly evolving technologies like machine learning and GenAI.
Translate complex technical concepts into clear, decision-useful information for non-technical stakeholders and regulators.
Track model risk issues and remediation plans while escalating material findings to leadership.
Respond to inquiries from regulators and lending partners regarding model and GenAI risks.
What we're looking for
Master’s degree in a quantitative field such as finance, mathematics, economics, statistics, or a related discipline.
4+ years of experience in model risk management, model validation, model governance, machine learning, data science, quantitative risk, AI governance, or a 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 a quantitative field of study (preferred).
5+ years of experience in model risk management, model governance, ML, Data Science, Risk, Trust and Safety, or Technical Writing (preferred).
Familiarity with GenAI applications including evaluation approaches, prompt/system design, RAG, tool use, guardrails, and monitoring (preferred).
Experience assessing models used outside of credit underwriting, such as fraud, compliance, finance, capital, liquidity, or operational risk (preferred).
Strong communication skills to adapt technical information for various audiences while protecting intellectual property (preferred).
Advanced coding skills in R, Python, and SQL, and experience using Git (preferred).
Generative AI
Machine Learning
Python
R
SAS
SQL
Vertex AI
LangChain
LangGraph
HuggingFace
OpenAI APIs
RAG
Prompt Engineering
Neural Networks
Model Risk Management
Actuarial Science
Data Science