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Quantitative Model Validation Analyst Jobs in Indiana

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Operations Analyst - Advance Planner

Crane, IN · On-site

$100 - $125/hr

Conduct quantitative analyses related to scheduling, workforce allocation, material availability, production performance, and operational readiness. * Develop predictive models, forecasts, and ...

Everwise Credit Union

Financial Analyst

South Bend, IN · On-site

Collect, validate, and organize financial data to ensure accuracy for system loads * Assist in ... Exceptional communication skills, to include verbal, written, visual, and quantitative * Adept at ...

Jj

Director, Analytics & Compliance

Warsaw, IN · On-site

Define governance standards for analytics, advanced analytics, automation, and AI-enabled solutions, including model validation, explainability, human oversight, access controls, lifecycle management ...

SOUL PROVIDER INC

Data Analyst

Westfield, IN · On-site

... aligns with and validates business results & KPI's * Analyze and interpret datasets using ... Bachelor's degree or equivalent practical experience in a quantitative field (Statistics, Data ...

Cover Care

Data Analyst

Westfield, IN

... aligns with and validates business results & KPI's * Analyze and interpret datasets using ... Bachelor's degree or equivalent practical experience in a quantitative field (Statistics, Data ...

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All JobsQuantitative Model Validation Analyst JobsQuantitative Model Validation Analyst Jobs in Indiana

Quantitative Model Validation Analyst information

See Indiana salary details

$53.8K

$127.4K

$228.4K

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

As of Sep 7, 2026, the average yearly pay for quantitative model validation analyst in Indiana is $127,393.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,100.00 and $138,500.00 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 Indiana?

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

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

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

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

Cities in Indiana 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 Indiana as of August 2026, with employment types broken down into 2% As Needed, 81% Full Time, 14% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $127,393 per year, or $61.2 per hour.

Computational Biologist - Quantitative Methods & Target Discovery

Scorpion Therapeutics

Indianapolis, IN • On-site

$150 - $200/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago

Job description

The Opportunity (Individual Contributor; Boston or Indianapolis)Responsibilities:
  • Independently design and implement end-to-end analyses of spatial and single-cell transcriptomic, proteomic, and metabolomic datasets, plus functional genomics workstreams.
  • Integrate results across modalities and with genetic evidence to build convergent frameworks for target prioritization.
  • Develop predictive models to score targets, distinguish association from mechanism, and provide confidence measures to inform portfolio decisions.
  • Advance quantitative toolkit: introduce ML/AI, knowledge graphs, Bayesian methods, and causal modeling where applicable.
  • Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics datasets.
  • Perform hands-on functional genomics analyses (CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate with transcriptomic/proteomic/pathway data for prioritization.
  • Collaborate cross-functionally to frame questions, translate computational outputs into discovery decisions, and co-develop models for drug discovery.
  • Champion standards for analytical rigor, reproducibility, and documentation; advise peers through reviews and shared problem-solving.
What You BringMinimum requirements:
  • Ph.D. in computational biology, biostatistics, biological engineering, systems biology, applied mathematics, or quantitative life science; training/research combining analytical method development (Bayesian approaches, AI/ML, etc.) with applied multi-omics, spatial omics, or functional genomics.
Preferred:
  • 2+ years post-doc or biopharma/biotech experience.
  • Experience with spatial omics, single-cell RNA-seq, proteomics, metabolomics, or multi-omics integration.
  • Proficiency in Python and/or R; solid software practices and scientific computing libraries.
  • Familiarity with workflow orchestration (e.g., Nextflow) and cloud-native environments.
  • Experience with at least two: Bayesian methods, causal modeling, knowledge graphs, ML/AI for target discovery, causal inference, or large-scale functional genomics.
Benefits (as stated):
  • Company bonus (company and individual performance dependent).
  • Comprehensive benefits: 401(k), pension, vacation, medical/dental/vision/prescription, flexible benefits, life insurance/death benefits, time off/leave, and well-being benefits.
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