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Model Validation Jobs in Arizona (NOW HIRING)

USAA

Model Validator (Mid-Level) - Fraud/AML

Phoenix, AZ · Hybrid

$93.77K - $179.24K/yr

Executes the model validation oversight process compliant with the written risk and compliance policies and procedures which require independence from model stewards (e.g., model owners, developers ...

General Motors

Senior ML Validation Engineer

Phoenix, AZ · On-site +1

$144.70K - $261.30K/yr

The Senior ML Validation Research Engineer will lead applied machine learning research focused on ... This role centers on simulation-based evaluation, uncertainty modeling, scenario coverage ...

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How much do model validation jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for model validation in Arizona is $48.45, according to ZipRecruiter salary data. Most workers in this role earn between $36.73 and $58.89 per hour, depending on experience, location, and employer.

What is a Model Validation job?

A Model Validation job involves assessing and verifying the accuracy, reliability, and performance of mathematical and statistical models used in finance, risk management, or other industries. Professionals in this role conduct independent testing, evaluate assumptions, and ensure models comply with regulatory and internal standards. They identify weaknesses, suggest improvements, and help mitigate potential risks associated with model usage. Model validators often work with machine learning models, credit risk models, or trading algorithms, depending on the industry.

What are the key skills and qualifications needed to thrive in the Model Validation position, and why are they important?

To thrive as a Model Validation professional, you need strong quantitative, statistical, and analytical skills, often supported by a degree in mathematics, statistics, finance, or a related field. Proficiency with programming languages such as Python or R, statistical modeling software, and familiarity with regulatory guidelines like SR 11-7 or CCAR is essential. Outstanding attention to detail, problem-solving abilities, and clear communication are valuable soft skills in this role. These competencies are crucial for rigorously assessing complex models, documenting findings, and collaborating effectively with model developers and risk management teams.

What are some common challenges faced by professionals in Model Validation roles?

One common challenge in Model Validation is staying up-to-date with evolving regulatory requirements and industry best practices, which can impact how models should be tested and documented. Model validators often work with highly complex financial or risk models, requiring strong analytical skills to assess underlying assumptions and potential risks. Additionally, balancing the need for thoroughness with tight deadlines and collaborating with model developers to address issues can be demanding. However, overcoming these challenges offers valuable opportunities to build expertise, work cross-functionally, and play a critical role in ensuring the integrity and reliability of key business decisions.
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Model Validation jobs near you

Data Scientist II - Model Validation and Monitoring

United IT

Scottsdale, AZ • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Data Scientist II – Model Validation and Monitoring

This position serves as a data science team member in the Model Validation and Monitoring Team delivering leading edge machine learning models to our clients. This includes providing effective challenges to model development, conduct model monitoring and performance tracking, provide root cause analysis of model performance, exploring, building, validating, and deploying models.

Essential functions include:

  • Lead model monitoring activities, including tracking performance metrics, detecting model and data drift, identifying data quality issues, providing root cause analysis, and recommending remediation strategies.
  • Conduct rigorous model validation by providing effective challenges during model development phases, including performance testing, benchmarking, provide remediation plan, and documentation to ensure models meet business, technical, and regulatory standards.
  • Explore and aggregate data independently to uncover data anomalies that impact algorithm performance
  • Write production level code in a dynamic, start-up environment
  • Solve complex problems using terabyte size data sets
  • Apply of a variety of machine learning techniques to a business problem to arrive at optimal approach
  • Partner with Product and Engineering teams to solve problems and identify trends and opportunities
  • Explain and visualize results and algorithm performance to non-technical audiences

Minimum qualifications include:

  • A minimum of 2 years of data science, engineering, mathematics, or related work experience is required.
  • Experience developing data science pipelines & workflows in Python, R or equivalent programming language. Experience in writing and tuning SQL. Experience handling terabyte size datasets with Spark language.
  • Experience applying various machine learning techniques, and understanding the key parameters that affect model performance
  • Experience using ML libraries, such as scikit-learn, mllib, etc.
  • Experience using data visualization tools
  • Able to write production level code, which is well-written and explainable
  • Ability to effectively communicate findings from complex analyses to non-technical audiences.

Preferred qualifications include:

  • Experience of using advanced ML algorithms building, testing, and deploying fraud models.

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