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Executive Quantitative Modeling Jobs (NOW HIRING)

iShare Inc

SENIOR MODELING LEAD

Houston, TX · On-site

Hands on experience in Model Development, Model Monitoring of advanced ML/quantitative/finance ... for executive and non-technical stakeholders. * Drive evaluation and adoption of emerging AI/ML ...

Kizuki International LLC

Research Analyst

Redmond, WA · On-site

$72K/yr

... executive decision-making. (10%) Utilize econometric, statistical, and quantitative modeling techniques to develop and refine analytical frameworks used for financial forecasting, operational ...

iShare, Inc.

SENIOR MODELING LEAD

Houston, TX · On-site

$145K - $165K/yr

Hands on experience in Model Development, Model Monitoring of advanced ML/quantitative/finance ... for executive and non-technical stakeholders. * Drive evaluation and adoption of emerging AI/ML ...

Nrg Bluewater Wind

Senior Director, Market Risk

Princeton, NJ · On-site

$201.12 - $331.80/hr

... quantitative analytics, portfolio valuation insight, model oversight, stress testing, and executive reporting to support informed decision‑making across NRG's commercial and risk management ...

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Executive Quantitative Modeling information

What are some common challenges faced by professionals in Executive Quantitative Modeling roles and how are they typically addressed?

Professionals in Executive Quantitative Modeling roles often face the challenge of translating complex quantitative models into actionable insights for stakeholders who may not have technical backgrounds. Balancing model sophistication with interpretability is key, as is ensuring data quality and regulatory compliance. Collaboration with cross-functional teams, such as IT, risk, and business units, is essential to integrate models into business processes and to gain buy-in from decision-makers. Regular communication, thorough documentation, and ongoing validation of model performance help address these challenges effectively.

What is the difference between Executive Quantitative Modeling vs Quantitative Analyst?

AspectExecutive Quantitative ModelingQuantitative Analyst
CredentialsAdvanced degrees (MBA, PhD), certifications like CFA or FRMBachelor's or Master's in Finance, Mathematics, or related fields
Work EnvironmentStrategic decision-making, senior management meetingsData analysis, model development, reporting
Industry UsageFinancial institutions, hedge funds, asset managementInvestment banks, asset managers, financial firms

Executive Quantitative Modeling professionals focus on high-level strategic models and decision-making, often working with senior leadership. Quantitative Analysts typically handle data analysis, model building, and implementation at a more technical level. Both roles require strong quantitative skills, but differ in scope and responsibilities.

What is executive quantitative modeling?

Executive quantitative modeling is a high-level role that involves developing, overseeing, and interpreting complex mathematical and statistical models to inform business strategies and decision-making. Professionals in this position typically lead teams that create models for risk assessment, financial forecasting, portfolio management, or pricing strategies. They work closely with senior executives to translate quantitative insights into actionable business plans. This role requires deep expertise in mathematical modeling, data analysis, and proficiency with advanced analytics tools and programming languages.

What are the key skills and qualifications needed to thrive as an Executive in Quantitative Modeling, and why are they important?

To excel as an Executive in Quantitative Modeling, you need advanced expertise in mathematical modeling, statistical analysis, and financial theory, typically supported by a graduate degree in a quantitative discipline such as mathematics, statistics, finance, or engineering. Proficiency with programming languages (like Python, R, or MATLAB), data analytics platforms, and experience with industry-standard risk management or financial modeling systems is highly valued. Leadership, strategic thinking, and strong communication skills set outstanding executives apart by enabling them to guide teams and translate complex models into actionable business insights. These capabilities are crucial for driving data-driven decision-making and maintaining a competitive edge in complex financial environments.
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What are the most commonly searched types of Quantitative Modeling jobs? The most popular types of Quantitative Modeling jobs are:
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Executive Quantitative Modeling jobs near you
Infographic showing various Executive Quantitative Modeling job openings in the United States as of July 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.

SENIOR MODELING LEAD

iShare Inc

Houston, TX • On-site

Full-time

Posted 29 days ago

Job description

 
Position: SENIOR MODELING LEAD
YEARS OF EXPERIENCE: 9-10+ YEARS
Location: Plano, TX & San Antonio, TX
Salary range: $145k-$165K+12% Incentive & Benefits.
 
Position Overview
We are seeking a Senior Modeling Lead to provide strategic and technical oversight across multiple AI/ML workstreams. In this role, you will direct a team of data scientists / modelers through full model development lifecycle, ensure consistency in modeling standards and governance practices, and serve as primary liaison between technical teams and senior stakeholders. Ideal candidate brings 9-10+ years of modeling experience and a proven ability to operate effectively at both strategic and technical levels.
  1. Relevant Team Lead experience (at least 3 years)in Banking domain
  2. Hands on experience in Model Development, Model Monitoring of advanced ML/quantitative/finance models
  3. Strong proficiency in Python; familiarity with modern AI/ML frameworks, ML-Ops, and large-scale data platforms.
 
Key Responsibilities
  • Provide strategic and technical leadership across multiple AI/ML modeling workstreams, ensuring alignment with business
  • Oversee full model lifecycle across all workstreams — including development, validation, independent assessment, performance optimization, monitoring, documentation, and governance.
  • Establish and enforce consistent modeling standards, methodology frameworks, explainability practices, and bias testing protocols across team.
  • Act as a primary escalation point for model risk, governance issues, technical challenges, partnering with risk, compliance as needed.
  • Lead model review and approval processes, ensuring all models meet internal governance requirements and applicable regulatory standards.
  • Translate complex modeling outcomes and technical findings into clear, actionable insights for executive and non-technical stakeholders.
  • Drive evaluation and adoption of emerging AI/ML tools, platforms, and methodologies, providing evidence-based recommendations to leadership.
  • Recruit, mentor, and develop a high-performing team of senior and mid-level modelers, fostering a culture of technical rigor and continuous improvement.
  • Define team's long-term analytical roadmap, balancing innovation with BAU delivery.
  • Collaborate cross-functionally with data engineering, technology, operations, and business teams to align modeling solutions with broader organizational objectives.
Qualifications & Experience
  • 10+ years of progressive experience in quantitative modeling, data science, or applied AI/ML, with at least 3 years in a team leadership or senior technical lead capacity.
  • Broad expertise across multiple modeling domains, such as predictive analytics, NLP, optimization, or AI platform evaluation.
  • Strong proficiency in Python; familiarity with modern AI/ML frameworks, MLOps tooling, and large-scale data platforms.
  • Deep understanding of end-to-end model lifecycle, including model risk management, validation frameworks, and regulatory expectations.
  • Proven ability to lead and develop cross-functional modeling teams in a fast-paced, delivery-oriented environment.
  • Experience engaging with model risk, audit, compliance, or regulatory stakeholders, and navigating governance and approval processes.
  • Exceptional communication skills & ability to present complex technical concepts clearly to stakeholders.
  • Track record of driving adoption of emerging AI/ML technologies in a structured manner.
  • Advanced degree (Master’s or PhD) in Statistics, Computer Science, Mathematics, Data Science, or a related quantitative discipline preferred.