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Quantitative Model Validation Analyst Jobs in Minnesota

UnitedHealth Group

Senior Data Scientist

Minnetonka, MN · Remote

$91K - $163K/yr

This role focuses on hands-on analysis, modeling, and stakeholder support, contributing to key ... Bachelor's Degree in a quantitative field such as Statistics, Health Economics, Mathematics ...

UnitedHealth Group

Senior Data Scientist

Minnetonka, MN · On-site

$91K - $163K/yr

This role focuses on hands-on analysis, modeling, and stakeholder support, contributing to key ... Bachelor's Degree in a quantitative field such as Statistics, Health Economics, Mathematics ...

Socket.dev

Senior Credit Strategy Analyst

Minneapolis, MN · On-site

$120 - $180/hr

Partner with Data Science on model development, performance assessments, and model-strategy ... Elite quantitative & analytical skills. Proven ability to drive analytical projects and insights ...

New

Howden Group

Associate Director

Edina, MN

$175K/yr

Execute upon all aspects of catastrophe modeling, including data preparation and validation, portfolio analysis and postprocessing, reporting of results, data visualization and mapping. * Be ...

AGCO

Validation Engineer

Jackson, MN · On-site

$54K - $100K/yr

Analyze collected data and write reports to effectively communicate and archive technical ... You will work with your wonderful AGCO colleagues in the Onsite model from Jackson (USA Jackson)

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All JobsQuantitative Model Validation Analyst JobsQuantitative Model Validation Analyst Jobs in Minnesota

Quantitative Model Validation Analyst information

See Minnesota salary details

$55.3K

$131.1K

$235.1K

How much do quantitative model validation analyst jobs pay per year?

As of Sep 4, 2026, the average yearly pay for quantitative model validation analyst in Minnesota is $131,121.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,200.00 and $142,500.00 per year, depending on experience, location, and employer.

What is a quantitative model validation analyst?

Quantitative Model Validation Analysts are professionals who assess and validate financial models used by banks and financial institutions. They ensure that these models are accurate, reliable, and comply with regulatory standards. Their work involves testing model assumptions, reviewing model methodologies, and analyzing model outputs to identify potential risks or weaknesses. By providing an independent review, they help organizations maintain the integrity and performance of their risk management and financial forecasting tools.

What are some typical challenges faced by quantitative model validation analysts when assessing complex financial models?

Quantitative Model Validation Analysts often encounter challenges such as interpreting intricate model methodologies, ensuring data integrity, and effectively communicating technical findings to stakeholders who may not have a quantitative background. Additionally, staying current with evolving regulatory requirements and industry standards can be demanding. Collaborating closely with model developers, risk managers, and auditors is crucial to address model limitations and propose actionable improvements, making strong communication and analytical skills essential for success in this role.

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

To thrive as a Quantitative Model Validation Analyst, you need a strong background in quantitative finance, statistics, and programming, typically supported by a degree in mathematics, finance, or a related field. Familiarity with statistical software such as Python, R, MATLAB, and model risk management frameworks is essential, and certifications like FRM or CFA are advantageous. Analytical thinking, attention to detail, and effective communication skills set top performers apart by enabling them to explain complex model risks and recommendations clearly. These skills and qualities are vital for ensuring the accuracy, reliability, and regulatory compliance of financial models within an organization.

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

AspectQuantitative Model Validation AnalystQuantitative Risk Analyst
CredentialsTypically requires a degree in finance, mathematics, or statistics; certifications like CFA or FRM are commonSimilar credentials; often holds CFA, FRM, or related certifications
Work EnvironmentFocuses on validating models used in risk management, trading, or credit scoring within financial institutionsAnalyzes and manages financial risk, including market, credit, and operational risks in banking or investment firms
Industry UsageCommonly employed in banking, asset management, and insurance sectorsWidely used in banking, hedge funds, and financial services

The main difference is that Quantitative Model Validation Analysts focus on testing and validating models to ensure accuracy and compliance, while Quantitative Risk Analysts assess and manage overall financial risks. Both roles require strong quantitative skills and often overlap in credentials and work environments, but their core responsibilities differ in scope and focus.

What are popular job titles related to Quantitative Model Validation Analyst jobs in Minnesota?

For Quantitative Model Validation Analyst jobs in Minnesota, the most frequently searched job titles are:

What job categories do people searching Quantitative Model Validation Analyst jobs in Minnesota look for?

The top searched job categories for Quantitative Model Validation Analyst jobs in Minnesota are:

What cities in Minnesota are hiring for Quantitative Model Validation Analyst jobs?

Cities in Minnesota with the most Quantitative Model Validation Analyst job openings:

Quantitative Model Validation Analyst jobs near you
Infographic showing various Quantitative Model Validation Analyst job openings in Minnesota as of August 2026, with employment types broken down into 2% As Needed, 86% Full Time, 10% Part Time, and 2% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $131,121 per year, or $63 per hour.

Principal Data Engineer - Enterprise Data & Analytics - Remote

Mayo Clinic

Rochester, MN • On-site, Remote

$111K - $134K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 6 days ago

Key responsibilities

  • Design, develop, review, and optimize production data pipelines, integrations, and transformations.

  • Provide technical leadership and mentorship to engineering teams in implementing enterprise-scale data architecture and solutions.

  • Partner with product owners and analytics teams to identify data needs, conduct exploratory analysis, build analytical models, and translate insights into actionable recommendations.

Mayo Clinic rating

7.8

Company rating: 7.8 out of 10

Based on 705 frontline employees who took The Breakroom Quiz

135th of 898 rated healthcare providers

Job description

Why Mayo Clinic

Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans - to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic.

Benefits Highlights
  • Medical: Multiple plan options.
  • Dental: Delta Dental or reimbursement account for flexible coverage.
  • Vision: Affordable plan with national network.
  • Pre-Tax Savings: HSA and FSAs for eligible expenses.
  • Retirement: Competitive retirement package to secure your future.

Responsibilities

The Principal Data Engineer serves as a hands-on technical authority responsible for defining and implementing enterprise-scale data architecture and engineering strategies while actively contributing to solution design, development, optimization, and technical delivery. As part of an assigned product team, this role develops and deploys data pipelines, integrations, and transformations to support analytics and machine learning applications using open-source programming languages and vendor software. The position requires a strong understanding of the organization's current solutions, coding languages, tools, and Enterprise Data and Analytics technology framework, as well as the ability to apply independent judgment, provide consultative services to departments, divisions, and leadership committees, and partner with product owners and Analytics and Machine Learning delivery teams to identify and retrieve data, conduct exploratory analysis, transform data, visualize trends, build and validate analytical models, and translate qualitative and quantitative assessments into actionable insights.


Key responsibilities:
These positions are hands-on engineering roles. In this role, employees are expected to actively design, develop, review, and optimize production code and platform capabilities while providing technical leadership and mentorship to engineering teams.
 


Qualifications

A Bachelor's degree in a relevant field such as engineering, mathematics, computer science, information technology, health science, or other analytical/quantitative field and a minimum of seven years of professional or research experience in data visualization, data engineering, analytical modeling techniques; OR an Associate's degree in a relevant field such as engineering, mathematics, computer science, information technology, health science, or other analytical/quantitative field and a minimum of nine years of professional or research experience in data visualization, data engineering, analytical modeling techniques. In-depth business or practice knowledge will also be considered. 

Incumbent must have the ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends and enterprise changes. Interpersonal skills, time management skills, and demonstrated experience working on cross functional teams are required. Requires strong analytical skills and the ability to identify and recommend solutions and a commitment to customer service. The position requires excellent verbal and written communication skills, attention to detail, and a high capacity for learning and problem resolution. Advanced experience in SQL is required. Advanced Experience in scripting languages such as Python, JavaScript, PHP, C++ or Java & API integration is required. Experience in hybrid data processing methods (batch and streaming) such as Apache Spark, Hive, Pig, Kafka is required. Experience with big data, statistics, and machine learning is required. The ability to navigate linux and windows operating systems is required. Knowledge of workflow scheduling (Apache Airflow Google Composer), Infrastructure as code (Kubernetes, Docker) CI/CD (Jenkins, Github Actions) is required. Experience in DataOps/DevOps and agile methodologies is required. Experience with hybrid data virtualization such as Denodo is preferred. Working knowledge of Tableau, Power BI, SAS, ThoughtSpot, DASH, d3, React, Snowflake, SSIS, and Google Big Query is preferred. 

The preferred candidate will possess:

  • Expert-level proficiency in Python and SQL with extensive experience developing enterprise-scale production systems.
  • Advanced expertise in scalable distributed computing frameworks and modern data processing platforms.
  • Advanced experience implementing and governing open data architectures utilizing Apache Iceberg, Delta Lake, Apache Hudi, and related technologies.
  • Deep understanding of modern analytical storage formats including Parquet, Avro, and ORC.
  • Demonstrated expertise in lakehouse architecture, data platform design, and large-scale data engineering practices.
  • Experience architecting and implementing cloud-agnostic solutions across multiple technology ecosystems.
  • Experience designing highly scalable, fault-tolerant, secure, and observable data platforms supporting analytics, AI, machine learning, and operational workloads.
  • Experience establishing enterprise engineering standards, architecture patterns, and modernization strategies.

Exemption Status
Exempt
Compensation Detail
$155,500.80 - $225,492.80/ year. Education, experience and tenure may be considered along with internal equity when job offers are extended.
Benefits Eligible
Yes
Schedule
Full Time
Hours/Pay Period
80
Schedule Details
M-F daytime hours 100% remote role, the employee needs to live within the US.
Weekend Schedule
As business needs dictate
International Assignment
No
Site Description
Just as our reputation has spread beyond our Minnesota roots, so have our locations. Today, our employees are located at our three major campuses in Phoenix/Scottsdale, Arizona, Jacksonville, Florida, Rochester, Minnesota, and at Mayo Clinic Health System campuses throughout Midwestern communities, and at our international locations. Each Mayo Clinic location is a special place where our employees thrive in both their work and personal lives. Learn more about what each unique Mayo Clinic campus has to offer, and where your best fit is. 

Equal Opportunity

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, protected veteran status or disability status. Learn more about the 'EOE is the Law'.  Mayo Clinic participates in E-Verify and may provide the Social Security Administration and, if necessary, the Department of Homeland Security with information from each new employee's Form I-9 to confirm work authorization.

Recruiter
Laura PercivalQualifications:

A Bachelor's degree in a relevant field such as engineering, mathematics, computer science, information technology, health science, or other analytical/quantitative field and a minimum of seven years of professional or research experience in data visualization, data engineering, analytical modeling techniques; OR an Associate's degree in a relevant field such as engineering, mathematics, computer science, information technology, health science, or other analytical/quantitative field and a minimum of nine years of professional or research experience in data visualization, data engineering, analytical modeling techniques. In-depth business or practice knowledge will also be considered. 

Incumbent must have the ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends and enterprise changes. Interpersonal skills, time management skills, and demonstrated experience working on cross functional teams are required. Requires strong analytical skills and the ability to identify and recommend solutions and a commitment to customer service. The position requires excellent verbal and written communication skills, attention to detail, and a high capacity for learning and problem resolution. Advanced experience in SQL is required. Advanced Experience in scripting languages such as Python, JavaScript, PHP, C++ or Java & API integration is required. Experience in hybrid data processing methods (batch and streaming) such as Apache Spark, Hive, Pig, Kafka is required. Experience with big data, statistics, and machine learning is required. The ability to navigate linux and windows operating systems is required. Knowledge of workflow scheduling (Apache Airflow Google Composer), Infrastructure as code (Kubernetes, Docker) CI/CD (Jenkins, Github Actions) is required. Experience in DataOps/DevOps and agile methodologies is required. Experience with hybrid data virtualization such as Denodo is preferred. Working knowledge of Tableau, Power BI, SAS, ThoughtSpot, DASH, d3, React, Snowflake, SSIS, and Google Big Query is preferred. 

The preferred candidate will possess:

  • Expert-level proficiency in Python and SQL with extensive experience developing enterprise-scale production systems.
  • Advanced expertise in scalable distributed computing frameworks and modern data processing platforms.
  • Advanced experience implementing and governing open data architectures utilizing Apache Iceberg, Delta Lake, Apache Hudi, and related technologies.
  • Deep understanding of modern analytical storage formats including Parquet, Avro, and ORC.
  • Demonstrated expertise in lakehouse architecture, data platform design, and large-scale data engineering practices.
  • Experience architecting and implementing cloud-agnostic solutions across multiple technology ecosystems.
  • Experience designing highly scalable, fault-tolerant, secure, and observable data platforms supporting analytics, AI, machine learning, and operational workloads.
  • Experience establishing enterprise engineering standards, architecture patterns, and modernization strategies.

What Mayo Clinic employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Mayo Clinic

Sourced by ZipRecruiter

Mayo Clinic is the largest integrated, not-for-profit medical group practice in the world. We're building the future, one where the best possible care is available to everyone — and more people can heal at home. Our relentless research turns into earlier diagnoses and new cures. That's how we inspire hope in those who need it most. At Mayo Clinic, experts work together to solve the most challenging unmet needs of patients. Our history of innovation dates back almost 150 years, when brothers Will and Charlie Mayo pioneered an integrated, team-based approach to medicine. Today, that trailblazing spirit drives innovations like Mayo Clinic Platform — which powers new technologies to change how care is delivered to all.

Industry

Hospitals

Company size

10,000+ Employees

Headquarters location

Rochester, MN, US

Year founded

1919

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