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Model Risk Manager Jobs in Oregon (NOW HIRING)

Staff Machine Learning Model Risk Specialist

OR · On-site +1

$98K/yr

This work is essential for Upstart's internal risk management, ensuring that our models help us make better decisions and maintain credibility with external stakeholders such as our regulators and ...

This individual need to provide hands-on development of risk models involving interconnection ... Manage the daily risk management processes to ensure the application is being adhered to ...

This individual need to provide hands-on development of risk models involving interconnection ... Manage the daily risk management processes to ensure the application is being adhered to ...

Credit Risk Analyst

Tigard, OR · On-site

$90K - $120K/yr

Work with Model Risk Management to ensure models are validated and remediate any findings.Develop and perform ongoing model monitoring tests and procedures evaluating the continued validity of ...

Upstart's Risk Capital team manages the co-investments that Upstart engages with its funding ... Mentor two junior analysts on the analysis, the modeling, and the judgment behind the numbers.

You will report to the Senior Manager, Asset Quality & Model Oversight and work closely with Credit Risk, Capital Markets, Model Risk Management, Operations, and other secured lending partners. How ...

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Model Risk Manager information

See Oregon salary details

$54.5K

$117.9K

$179.7K

How much do model risk manager jobs pay per year?

As of Sep 10, 2026, the average yearly pay for model risk manager in Oregon is $117,947.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,200.00 and $136,400.00 per year, depending on experience, location, and employer.

What does a model risk manager do?

A Model Risk Manager is responsible for identifying, assessing, and mitigating risks associated with financial and analytical models used by an organization. They ensure that models are accurate, reliable, and compliant with regulatory standards by overseeing validation processes and monitoring model performance. Their role often includes collaborating with model developers, conducting independent reviews, and implementing model governance frameworks to minimize potential losses or errors stemming from model misuse or inaccuracies.

What skills and qualifications are needed to be a model risk manager?

To thrive as a Model Risk Manager, you need a solid background in quantitative finance, statistics, or mathematics, often supported by an advanced degree and experience in model development or validation. Familiarity with programming languages such as Python or R, risk management frameworks, and regulatory requirements like SR 11-7 or ECB guidelines is typically expected. Strong analytical thinking, attention to detail, and effective communication are crucial soft skills for articulating complex model risks to stakeholders. These competencies are vital for ensuring the accuracy, compliance, and reliability of financial models within an organization.

What are common challenges a model risk manager faces when validating complex financial models?

Model Risk Managers often encounter challenges such as limited or incomplete data, evolving regulatory requirements, and the need to validate highly complex or proprietary models. They must work closely with model developers, quantitative analysts, and compliance teams to ensure all assumptions and methodologies are sound. Staying up to date with industry best practices and maintaining clear documentation are also crucial, as is effectively communicating findings to both technical and non-technical stakeholders.

What is the difference between Model Risk Manager vs Quantitative Analyst?

AspectModel Risk ManagerQuantitative Analyst
Required CredentialsAdvanced degrees in finance, statistics, or mathematics; certifications like FRM or CFADegree in finance, economics, mathematics, or related fields; often CFA or CQF
Work EnvironmentFocus on risk management teams within financial institutions; regulatory complianceAnalytical roles within trading, investment, or banking divisions; model development
Employer & Industry UsageFinancial institutions, banks, asset managersInvestment firms, hedge funds, banks, financial services

The Model Risk Manager primarily oversees and mitigates risks associated with financial models, ensuring compliance and accuracy. In contrast, Quantitative Analysts develop and implement models to support trading, investment, or risk strategies. While both roles require strong quantitative skills and similar credentials, their focus areas differ—risk management versus model development and analysis.

What are popular job titles related to Model Risk Manager jobs in Oregon?

For Model Risk Manager jobs in Oregon, the most frequently searched job titles are:

What cities in Oregon are hiring for Model Risk Manager jobs?

Cities in Oregon with the most Model Risk Manager job openings:

Infographic showing various Model Risk Manager job openings in Oregon as of August 2026, with employment types broken down into 84% Full Time, 15% Part Time, and 1% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $117,947 per year, or $56.7 per hour.

Staff Machine Learning Model Risk Specialist

OR • On-site, Remote

$98K/yr

Full-time

Posted 23 days ago


Upstart rating

7.6

Company rating: 7.6 out of 10

Based on 6 frontline employees who took The Breakroom Quiz


Job description

The Team: 

Upstart's Model Risk team is responsible for ensuring that the risk of models - including all models impacting the new Upstart Bank - is well-understood, monitored, and mitigated. For years, machine learning (ML) models have been the key, differentiating technology at Upstart and an exciting area of focus for the team, but we are also expanding our scope to include all modeling methodologies and Generative AI applications across the Bank. This work is essential for Upstart's internal risk management, ensuring that our models help us make better decisions and maintain credibility with external stakeholders such as our regulators and capital providers. The team's focus is on articulating sound model risk management principles and implementing them in collaboration with our peers on Upstart's Risk and Machine Learning teams. This work also includes explaining our models to stakeholders, supporting external validations, and conducting analyses to reinforce our goals.

As a Staff Model Risk Specialist at Upstart, you will independently execute core components of the model risk management program supporting Upstart Bank. You will oversee risk across a diverse and growing inventory of models and Generative AI applications, including sophisticated machine learning models used in lending and other models supporting areas such as fraud, compliance, finance, capital and liquidity, servicing, and operational risk.

This presents a unique opportunity to help build a comprehensive model risk management program for a new bank. You will evaluate model and GenAI application documentation, monitoring, governance, and risk assessments while partnering with developers, business sponsors, and risk stakeholders to identify and address emerging risks. You will apply a risk-based approach across technologies that range from traditional statistical methods to advanced machine learning and GenAI systems, adapting your review to their different purposes, complexities, and risk profiles. You will also translate complex technical concepts into clear, decision-useful information for audiences with varying levels of technical expertise.

How you'll make an impact

  • 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 for models and GenAI applications affecting Upstart Bank.
  • Review methodologies, assumptions, data inputs, system designs, performance measures, controls, and limitations to provide effective challenge and identify areas requiring further analysis or remediation.
  • Apply a risk-based approach to evaluate a broad range of quantitative methods and technologies, from traditional statistical and financial models to complex machine learning models and GenAI applications.
  • Conduct and document model risk assessments, monitoring reviews, and targeted quantitative analyses that support internal policies and regulatory expectations.
  • Help develop practical governance approaches for new and rapidly evolving technologies, particularly machine learning and GenAI applications for which risks, evaluation methods, and industry practices continue to evolve.
  • Respond to model- and GenAI-related questions from regulators, lending partners, and other external stakeholders in collaboration with Machine Learning, business teams, Legal, Compliance, and partner-facing teams.
  • Track model risk issues, remediation plans, program goals, and emerging risks, escalating material findings and recommending practical improvements as the Bank's model inventory and use cases continue to expand.

Minimum Qualifications 

  • Master's degree in 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

Preferred Qualifications

  • PhD in a quantitative field of study such as statistics, econometrics, finance, mathematics; or a 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, and managing trade-offs such as providing modeling detail while protecting intellectual property
  • Proactive mindset with the 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 the use of machine learning and GenAI in a regulated environment.

Position location This role is available in the following locations: Remote

Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions' cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

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