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Model Risk Jobs in Utah (NOW HIRING)

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Sr Catastrophe Risk Analyst

Draper, UT · On-site

Modeling and analytics are not a support function--they are core strategic capability dictates how we grow, price risk, optimize our portfolio, and maintain a sustainable competitive advantage. The ...

Morgan Stanley

E*TRADE Risk Officer

Sandy, UT · On-site

Ensure continuous and appropriate supervisory coverage, working with Management, Site Leaders, and the Senior Risk Officer to maintain coverage models and back‑up plans. * Oversee adherence to the ...

Morgan Stanley

E*TRADE Risk Officer

Sandy, UT · On-site

Ensure continuous and appropriate supervisory coverage, working with Management, Site Leaders, and the Senior Risk Officer to maintain coverage models and back-up plans. Oversee adherence to the E*

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Model Risk information

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$13

$27

$67

How much do model risk jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for model risk in Utah is $27.62, according to ZipRecruiter salary data. Most workers in this role earn between $17.74 and $35.24 per hour, depending on experience, location, and employer.

What is model risk?

Model risk refers to the potential for adverse consequences resulting from decisions based on incorrect or misused models. In financial institutions, model risk can arise if a model's assumptions are flawed, if the data input is poor, or if the model is applied inappropriately. Managing model risk involves validating models, monitoring their performance, and ensuring that they are used within their intended scope. Effective model risk management helps organizations avoid significant financial losses and comply with regulatory requirements.

What are some typical challenges faced by professionals working in model risk, and how can they be addressed?

Professionals in Model Risk often encounter challenges such as ensuring model accuracy, managing regulatory compliance, and effectively communicating complex technical findings to non-technical stakeholders. Addressing these challenges requires a strong understanding of both quantitative modeling and relevant regulations, as well as strong collaboration skills to work with model developers, auditors, and business units. Staying informed about evolving regulatory standards and participating in ongoing training can also help model risk professionals remain effective and add value to their organizations.

What are the key skills and qualifications needed to thrive as a model risk analyst, and why are they important?

To thrive as a Model Risk Analyst, you need a solid background in quantitative analysis, statistics, or finance, often supported by an advanced degree in a related field. Familiarity with model validation tools, programming languages such as Python or R, and regulatory frameworks like SR 11-7 is essential. Strong analytical thinking, attention to detail, and effective communication skills are crucial for evaluating models and presenting findings to stakeholders. These skills ensure model integrity, regulatory compliance, and risk mitigation in financial institutions.

What is the difference between Model Risk vs Model Validation?

AspectModel RiskModel Validation
Primary FocusIdentifying, assessing, and mitigating risks associated with modelsEvaluating and testing models to ensure accuracy and reliability
Required CredentialsQuantitative skills, risk management certifications, industry experienceQuantitative expertise, validation certifications, industry knowledge
Work EnvironmentRisk management teams within financial institutions or firmsModel validation teams, often within risk or model development departments
Industry UsageUsed across banking, insurance, and investment firms to manage model-related risksCommonly employed in financial services to verify model performance

Model Risk focuses on managing the potential negative impacts of models, including errors and misuse, while Model Validation concentrates on testing and confirming the accuracy and robustness of models. Both roles are essential in financial industries to ensure models are reliable and risks are minimized.

What does a model risk do?

A model risk professional assesses and manages the risks associated with using mathematical and statistical models in financial and operational decision-making. They review model accuracy, validate assumptions, and ensure compliance with regulatory standards, often using tools like SAS or R. Their work helps prevent financial loss due to model errors or misestimations.

What does a model risk specialist do?

A model risk specialist evaluates and manages risks associated with financial or operational models used by organizations. They review model assumptions, validate model performance, and ensure compliance with regulatory standards, often using statistical and analytical tools. Their work helps prevent model errors that could lead to financial loss or regulatory issues.

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

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

What job categories do people searching Model Risk jobs in Utah look for?

The top searched job categories for Model Risk jobs in Utah are:

Model Risk jobs near you
Infographic showing various Model Risk job openings in Utah as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $57,444 per year, or $27.6 per hour.

Asset & Wealth Management-Salt Lake City-Vice President, Quantitative Engineering-10412228

Salt Lake City, UT • On-site

Goldman Sachs, Inc.
Finance and Insurance • 10K+ employees
Learn more about Goldman Sachs, Inc.external

$174K - $224K/yr

Full-time

Re-posted 2 days ago

Goldman Sachs rating

7.8

Company rating: 7.8 out of 10

Based on 28 frontline employees who took The Breakroom Quiz

Job description

Job Duties: Vice President, Quantitative Engineering with Goldman Sachs & Co. LLC in Salt Lake City, Utah. Lead the development, implementation, and documentation of scenarios comprised of a broad range of economic and financial variables for businesses within the Firm. Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues. Analyze large data sets (structured and unstructured) to build predictive models of business-relevant market variables. Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, current events, statistical analysis, and programming. Build and challenge risk models, identify and quantify vulnerabilities across market, credit, liquidity risk and modeling. Create and maintain clear and complete technical documentation of the risk-model performance testing approach and process. Mentor junior and mid-level team members.

Job Requirements: Master's degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Computational Finance, Applied Mathematics, or related quantitative field and three (3) years of experience in job offered or a related quantitative engineering role OR Bachelor's degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Computational Finance, Applied Mathematics, or related quantitative field and five (5) years of experience in job offered or a related quantitative engineering role OR PhD degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Computational Finance, Applied Mathematics, or related quantitative field and one (1) year of experience in job offered or a related quantitative engineering role. Prior experience must include three (3) years of experience (with a Master's degree) OR five (5) years of experience (with a Bachelor's degree) OR one (1) year of experience (with a PhD degree) with 5 of the 8 following skills: C++, Java, or Python; performing financial mathematics, including at least one of the following: stochastic calculus, no-arbitrage pricing theory, multivariable calculus, linear algebra, probability theory, numerical methods, or Monte-Carlo techniques; performing analysis leveraging market risk, credit risk, liquidity risk, or mathematical finance concepts; object-oriented programming and scripting programming languages such as Python or Java; implementing mathematical models or analytics in production-quality software; working with database query languages, such as SQL, MongoDB, or other data management tools to process large datasets; applying algorithms or data structures to write complex programs; and developing pricing models for financial products to model risk, economics, and cash flows under normal and distressed market environments.

The Goldman Sachs Group, Inc., 2026. All rights reserved. Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veteran status, disability, or any other characteristic protected by applicable law.

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About Goldman Sachs

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At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869

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