ZipRecruiter

Log In

1

Quantitative Model Validation Analyst Jobs in Dallas, TX

SoFi

Fraud Model Analyst

Frisco, TX · On-site

... validation materials, and audit responsesSupporting ongoing monitoring and reporting of vendor ... Bachelor's degree in a quantitative field such as Statistics, Mathematics, Economics, Engineering ...

TBK Bank, SSB

Director of Data Science

Dallas, TX · On-site

... or quantitative analytics. * Strong experience with forecasting, predictive modeling, and ... Experience validating models and monitoring performance in production environments. Direct ...

TBK Bank, SSB

Director of Data Science

Dallas, TX · On-site

... or quantitative analytics. * Strong experience with forecasting, predictive modeling, and ... Experience validating models and monitoring performance in production environments. Direct ...

TBK Bank, SSB

Director of Data Science

Dallas, TX · On-site

... or quantitative analytics. * Strong experience with forecasting, predictive modeling, and ... Experience validating models and monitoring performance in production environments. Direct ...

TBK Bank, SSB

Director of Data Science

Dallas, TX · On-site

... or quantitative analytics. * Strong experience with forecasting, predictive modeling, and ... Experience validating models and monitoring performance in production environments. Direct ...

USAA

Lead Analyst, Credit Risk Strategy

Plano, TX · On-site

May have model ownership responsibilities and drive accountability for quantitative model ... Advanced knowledge of data analysis tools including skills to develop analysis queries and ...

previous pagenext page

Showing results 21-40

All JobsQuantitative Model Validation Analyst JobsQuantitative Model Validation Analyst Jobs in Dallas, TX

Quantitative Model Validation Analyst information

See Dallas, TX salary details

$55.9K

$132.5K

$237.5K

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

As of Sep 6, 2026, the average yearly pay for quantitative model validation analyst in Dallas, TX is $132,492.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,300.00 and $144,000.00 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 Dallas, TX?

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

What job categories do people searching Quantitative Model Validation Analyst jobs in Dallas, TX look for?

The top searched job categories for Quantitative Model Validation Analyst jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Quantitative Model Validation Analyst jobs?

Cities near Dallas, TX 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 Dallas, TX as of August 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 81% In-person, 12% Hybrid, and 7% Remote job distribution, with an average salary of $132,492 per year, or $63.7 per hour.

Fraud Model Analyst

SoFi

Frisco, TX • On-site

Full-time

Re-posted yesterday

Job description

Employee Applicant Privacy Notice
Who we are:
Shape a brighter financial future with us.
Together with our members, we're changing the way people think about and interact with personal finance.
We're a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we're at the forefront. We're proud to come to work every day knowing that what we do has a direct impact on people's lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.
The role:
We are looking for a Fraud Model Analyst to join our Fraud Model Development team, with a focus on governance, oversight, and lifecycle management of third-party (vendor) fraud models. This role will be responsible for ensuring vendor models are compliant, well-documented, and effectively monitored within SoFi's fraud ecosystem.
This role will partner closely with Fraud Model Development, Fraud Strategy, Product, Operations, and Engineering to establish consistent analytical frameworks for measuring fraud performance, evaluating model and strategy changes, and identifying opportunities to improve fraud detection while minimizing false positives and member friction.
The individual will use large-scale fraud, transaction, and member data to evaluate model and strategy performance, conduct statistical and diagnostic analyses, design and analyze experiments, and develop scalable monitoring and measurement frameworks. The role will also contribute to fraud model development initiatives through feature analysis, performance benchmarking, threshold analysis, and production model evaluation.
The ideal candidate has a strong analytical and statistical mindset, is comfortable working with complex datasets using SQL and Python, understands predictive model performance, and can translate analytical findings into clear recommendations for technical and business stakeholders.
What you'll do:
The Fraud Model Analyst will help SoFi scale and govern vendor fraud models by:
Managing the end-to-end lifecycle of vendor fraud models, including onboarding, documentation, monitoring, and periodic reviewsPartnering with Model Risk Management (MRM), Legal, and Compliance teams to ensure adherence to governance and regulatory requirementsCoordinating with external vendors to obtain model documentation, technical details, and performance insightsAnalyzing model performance metrics (e.g., fraud capture, false positive rates, drift) and identifying risks or improvement opportunitiesInvestigating model behavior and data issues using SQL and internal datasets to support root cause analysisSupporting fraud model development initiatives by contributing to feature analysis, performance benchmarking, and strategy designCollaborating with Fraud Strategy, Data Science, and Engineering teams to integrate vendor models into fraud decisioning frameworksPreparing and maintaining model documentation, validation materials, and audit responsesSupporting ongoing monitoring and reporting of vendor model performance, including identifying degradation and recommending actionsActing as a bridge between Data Science, Engineering, Fraud Strategy, and Risk/Compliance teams to ensure alignmentManaging multiple models and timelines, ensuring timely delivery of governance and reporting requirements
What you'll need:
  • 3+ years of experience in data science, fraud analytics, risk analytics, model analytics, or another related quantitative role.
  • Bachelor's degree in a quantitative field such as Statistics, Mathematics, Economics, Engineering, Computer Science, Data Science, or equivalent experience.
  • Strong analytical and statistical skills with experience evaluating predictive model performance and identifying underlying drivers of performance changes.
  • Proficiency in SQL and Python for data analysis, statistical analysis, model evaluation, and investigation.
  • Experience working with fraud or predictive model performance metrics such as fraud capture rate, false-positive rate, precision/recall, AUC, drift, and other model and business performance measures.
  • Familiarity with data science and machine-learning workflows and the ability to work with datasets to support model analysis, benchmarking, monitoring, and validation.
  • Understanding of experimental design and statistical significance, with experience analyzing A/B tests, control/treatment groups, champion/challenger tests, backtests, or similar experiments.
  • Experience performing root-cause analysis, segmentation, cohort analysis, or other diagnostic analyses to identify drivers of performance changes.
  • Ability to evaluate model thresholds and understand trade-offs between fraud detection, false positives, member friction, operational impact, and business outcomes.
  • Experience working with large-scale transaction, member, fraud, or operational datasets.
  • Experience developing analytical reporting, dashboards, or monitoring frameworks using Tableau, Looker, Power BI, or similar tools.
  • Strong communication and data storytelling skills with the ability to translate technical and statistical concepts into clear business recommendations.
  • Experience working with cross-functional stakeholders across Data Science, Fraud Strategy, Product, Engineering, Operations, or Risk.
  • Strong organizational skills and the ability to manage multiple analytical initiatives and priorities in a fast-moving environment
Nice to have:
  • Experience working directly with fraud models or contributing to fraud model development.
  • Experience in payments fraud, account takeover, first-party fraud, transaction fraud, identity fraud, or financial crime analytics.
  • Familiarity with machine-learning concepts and common classification methodologies, with the ability to interpret model outputs, performance metrics, and trade-offs.
  • Experience with model monitoring, model drift analysis, backtesting, threshold optimization, segmentation, feature analysis, or champion/challenger frameworks.
  • Experience measuring the production impact and incremental value of machine-learning models.
  • Understanding of common fraud modeling and measurement challenges, including label maturity, delayed outcomes, class imbalance, changing fraud patterns, data leakage, and selection bias.
  • Experience with automated analytical workflows or reusable Python/SQL frameworks for model and fraud performance analysis.
  • Familiarity with Model Risk Management (MRM), model governance, documentation, and monitoring requirements.
  • Experience working with third-party/vendor fraud models and evaluating their performance alongside internally developed models.
  • Exposure to regulatory and compliance environments within financial services.

Compensation and Benefits
The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate's experience, skills, and location.
To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page!
SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.
The Company hires the best qualified candidate for the job, without regard to protected characteristics.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
New York applicants: Notice of Employee Rights
SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com.
We are unable to accommodate remote work from Hawaii, Alaska or Puerto Rico at this time.
Internal Employees
If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.

Apply