Lead Quantitative Consultant: Model Risk & AI Solutions

Forvis Mazars US; Dallas (TX); On-site; USD 65,000 - 102,000; Full time

Job summary

Forvis Mazars, LLP seeks a Quantitative Consultant to join the Quantitative & AI Solutions team. The role combines hands-on modeling with leadership to build, validate, govern, and run models in regulated environments for large financial institutions.

You will collaborate with Risk, Finance, and Technology stakeholders to strengthen model risk management, validation, and operations, covering CECL, ALM/IRR, and liquidity models, in a client-facing delivery setting.

Qualifications

• Bachelor's degree in finance, economics, statistics, mathematics, engineering, or computer science.
• Proficiency with Microsoft Office suite.

Responsibilities

• Validate and challenge model methodologies and assumptions.
• Review data inputs and modeling choices for completeness and justification.
• Test computational accuracy and conduct performance analyses.
• Validate models across CECL, ALM/IRR, capital and liquidity models and other risk domains.
• Document testing results and communicate conclusions to model owners and executives.
• Prioritize multiple workstreams and manage deadlines across engagements.
• Apply analytical rigor to complex model risk management issues and present findings clearly.
• Collaborate effectively with team members and clients in delivery environments.

Skills

Analytical thinking

Communication skills

Education

Bachelor's degree in quantitative field

Tools

Python

R

SQL

Excel

Job description

Forvis Mazars, LLP seeks a Quantitative Consultant to join the Quantitative & AI Solutions team. The role combines hands-on modeling with leadership to build, validate, govern, and run models in regulated environments for large financial institutions.

You will collaborate with Risk, Finance, and Technology stakeholders to strengthen model risk management, validation, and operations, covering CECL, ALM/IRR, and liquidity models, in a client-facing delivery setting.