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Operations Research Manager Jobs in Utah (NOW HIRING)

Research Specialists

Salt Lake City, UT · On-site

$14.38 - $27.87/hr

... operations. Key Responsibilities Develop, maintain, and optimize data processing pipelines for ... management, user and service configuration). Ability to execute defined tasks independently while ...

$22.75 - $30.25/hr

This position assists in managing clinical research studies of Intermountain Healthcare and is ... Laboratory Operations: Collect, process, maintain, and ship lab samples, demonstrating laboratory ...

This position assists in managing clinical research studies of Intermountain Healthcare and is ... Laboratory Operations: Collect, process, maintain, and ship lab samples, demonstrating laboratory ...

Manager, DevOps

Salt Lake City, UT · On-site

$51 - $70/hr

As programmers, researchers, designers, client professionals and craftspeople we create the tech, ... The DevOps Manager leads the DevOps team, overseeing developer tooling/support and maintaining ...

Manager, DevOps

Salt Lake City, UT

$51 - $70/hr

As programmers, researchers, designers, client professionals and craftspeople we create the tech, ... The DevOps Manager leads the DevOps team, overseeing developer tooling/support and maintaining ...

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Operations Research Manager information

What is the difference between Operations Research Manager vs Data Analyst?

AspectOperations Research ManagerData Analyst
Required CredentialsBachelor's or Master's in Operations Research, Industrial Engineering, or related fields; often certifications in project managementBachelor's or Master's in Statistics, Data Science, or related fields; certifications like Microsoft Excel or Tableau
Work EnvironmentTypically in corporate, manufacturing, or logistics settings; managing teams and projectsIn offices or remote; analyzing data sets and creating reports
Employer & Industry UsageUsed in supply chain, manufacturing, logistics, and consulting firmsCommon across finance, marketing, healthcare, and tech industries

While both roles involve data analysis, Operations Research Managers focus on optimizing complex systems and processes using advanced mathematical models, often leading teams. Data Analysts primarily interpret data to support decision-making through reports and visualizations. The roles overlap in data skills but differ in scope and strategic impact.

How do Operations Research Managers typically collaborate with cross-functional teams within an organization?

Operations Research Managers frequently work closely with teams from departments such as IT, finance, logistics, and production to identify operational challenges and develop data-driven solutions. They lead or participate in project meetings, translate complex analytical findings into actionable recommendations, and ensure that proposed models align with business objectives. Effective communication and the ability to explain technical concepts clearly are essential, as these managers serve as a bridge between analytical experts and decision-makers. This collaborative environment fosters innovation and ensures the successful implementation of optimization strategies.

What are Operations Research Managers?

Operations Research Managers are professionals who oversee teams that use mathematical modeling, data analysis, and optimization techniques to help organizations solve complex problems and make better decisions. They often manage projects that improve efficiency, reduce costs, and enhance overall performance in various industries such as manufacturing, logistics, healthcare, and finance. These managers translate business challenges into analytical models and guide their teams in implementing solutions that support organizational goals.

What are the key skills and qualifications needed to thrive as an Operations Research Manager, and why are they important?

To thrive as an Operations Research Manager, you need advanced analytical skills, a solid grasp of mathematical modeling, and typically a master's or Ph.D. in operations research, mathematics, or a related field. Proficiency in optimization software, statistical analysis tools (such as MATLAB, R, or Python), and experience with data visualization systems is essential. Strong leadership, problem-solving, and communication skills set standout managers apart by enabling them to lead teams and explain complex findings to stakeholders. These capabilities are crucial for making data-driven decisions that optimize organizational efficiency and competitive advantage.
What are the most commonly searched types of Operations Research jobs in Utah? The most popular types of Operations Research jobs in Utah are:
What are popular job titles related to Operations Research Manager jobs in Utah? For Operations Research Manager jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Operations Research Manager jobs in Utah look for? The top searched job categories for Operations Research Manager jobs in Utah are:
What cities in Utah are hiring for Operations Research Manager jobs? Cities in Utah with the most Operations Research Manager job openings:
Infographic showing various Operations Research Manager job openings in Utah as of June 2026, with employment types broken down into 85% Full Time, 13% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.
Engineering-Salt Lake City-Associate, Quantitative Engineering-046336

Engineering-Salt Lake City-Associate, Quantitative Engineering-046336

Goldman Sachs, Inc.

Salt Lake City, UT • On-site

Full-time

Posted 3 days ago


Goldman Sachs rating

8.2

Company rating: 8.2 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

30th of 142 rated banks


Job description


Job Duties: Associate, Quantitative Engineering with Goldman Sachs & Co. LLC in Salt Lake City, Utah. Multiple positions available. Develop, implement, and document 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.
Job Requirements: Master's degree (U.S. or foreign equivalent) in Computer Science/Engineering, Financial Engineering, Mathematical Finance, Applied Mathematics, Data Science, Operations Research or related quantitative field and one (1) year of experience in job offered or a related quantitative engineering role OR Bachelor's degree (U.S. or foreign equivalent) in Computer Science/Engineering, Financial Engineering, Mathematical Finance, Applied Mathematics, Data Science, Operations Research or related quantitative field and two (2) years of experience in job offered or a related quantitative engineering role. Prior experience must include one (1) year of experience (with a Master's degree) OR two (2) years of experience (with a Bachelor's degree) with 5 of the 7 following skills: C++, Java, or Python; developing probability and pricing models utilizing financial mathematics principles, including stochastic calculus, no-arbitrage pricing theory, partial differential equations, multivariable calculus, linear algebra, numerical methods, optimization, probability, or random processes; quantitative analysis and model development using advanced econometric, statistical, and mathematical techniques, including Bayesian analysis, time series analysis, or machine learning algorithms; performing risk management or scenario-based analysis; developing quantitative risk analytics, including factor models; developing rigorous and scalable data management and analysis tools to provide risk oversight and support the investment process; and statistics and data driven performance analysis, including Linear Regression or Time Series Analysis to measure performance.
©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

Sourced by ZipRecruiter

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