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Stochastic Modeling Jobs (NOW HIRING)

The Pennsylvania State University

STAT 416 - Stochastic Modeling * STAT 418 - Introduction to Probability and Stochastic Processes for Engineering * STAT 460 - Intermediate Applied Statistics * STAT 461 - Analysis of Variance * STAT ...

Midi Health

Staff Data Scientist

Palo Alto, CA · On-site +1

Build stochastic (Monte Carlo) macro-simulations to help leadership and finance stress-test our business model. You will answer questions like: "If a major insurance payer shifts an allowable rate in ...

TMS

Data Scientist

New York, NY · On-site

$60 - $62/hr

Familiarity with scenario analysis/stress-testing, simulation analysis, rare event modeling, and stochastic modeling preferred but not required. * Substantial experience with Python, R, and relevant ...

Midi Health

Staff Data Scientist

Palo Alto, CA · Hybrid

Build stochastic (Monte Carlo) macro-simulations to help leadership and finance stress-test our business model. You will answer questions like: "If a major insurance payer shifts an allowable rate in ...

Steadily

Catastrophe Risk Manager

Overland Park, KS · On-site

Continuously elevate pricing sophistication ; translate stochastic modeling, external data, and new analytics into sharper underwriting insight. Your Background * Required * 5+ years of catastrophe ...

The Pennsylvania State University

... Stochastic Modeling/STAT 418 - Introduction to Probability and Stochastic Processes for Engineering/STAT 460 - Intermediate Applied Statistics/STAT 461 - Analysis of Variance STAT 462 - Applied ...

Bank of America

Vice President; Structurer

New York, NY · On-site

$225K - $235K/yr

Develop and maintain proprietary pricing models leveraging stochastic processes to ensure accurate valuation under complex market conditions. * Build and optimize internal software tools for trade ...

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Stochastic Modeling information

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$25.5K

$104.4K

$201.5K

How much do stochastic modeling jobs pay per year?

As of Jul 16, 2026, the average yearly pay for stochastic modeling in the United States is $104,419.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,500.00 and $128,000.00 per year, depending on experience, location, and employer.

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

To thrive in stochastic modeling, you need strong mathematical and statistical skills, often backed by a degree in mathematics, statistics, data science, or a related field. Expertise with technical tools such as R, Python, MATLAB, and statistical modeling software, as well as familiarity with industry-standard certifications like FRM or CFA in finance, is highly valued. Analytical thinking, attention to detail, and the ability to communicate complex ideas to non-technical stakeholders are critical soft skills. These competencies allow you to create accurate models for forecasting and risk assessment, helping organizations make informed decisions in uncertain environments.

What are some typical daily responsibilities for a professional in stochastic modeling?

As a stochastic modeling professional, your daily tasks might include developing and validating probabilistic models, analyzing large datasets, and performing simulations to forecast outcomes or assess risks. You’ll often collaborate with cross-functional teams such as data scientists, financial analysts, or engineers to translate real-world problems into mathematical frameworks. Regular responsibilities also involve presenting findings to stakeholders, refining models based on new data, and staying updated on the latest modeling techniques in your industry. This role provides a balance of independent analytical work and teamwork, offering diverse challenges and skill development opportunities.

What is a Stochastic Modeling job?

A Stochastic Modeling job involves developing mathematical models that incorporate randomness to analyze uncertain systems and predict outcomes. Professionals in this field apply probability theory, statistics, and computational techniques to fields such as finance, insurance, engineering, and data science. They build and validate models to assess risks, optimize decision-making, and improve forecasting accuracy. Strong analytical skills and proficiency in programming languages like Python, R, or MATLAB are often required.

More about Stochastic Modeling jobs
What are the most commonly searched types of Stochastic Modeling jobs? The most popular types of Stochastic Modeling jobs are:
What job categories do people searching Stochastic Modeling jobs look for? The top searched job categories for Stochastic Modeling jobs are:
Stochastic Modeling jobs near you
Infographic showing various Stochastic Modeling job openings in the United States as of July 2026, with employment types broken down into 85% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $104,419 per year, or $50.2 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 9 days ago

Job description

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk 
  • Design and implement analytical tools and pipelines using Python and SQL 
  • Contribute to model validation, backtesting, and performance evaluation 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure 
  • Communicate complex quantitative insights through clear visualizations and technical summaries 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics 
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) 
  • Strong foundation in probability, statistics, and numerical methods 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL 
  • Experience working with large datasets and implementing quantitative models 
  • Ability to think rigorously about complex systems and translate theory into practical solutions 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) 
  • Experience with scientific computing, optimization, or machine learning 
  • Background or research in cross-disciplinary areas such as: 
    • Statistical physics, complex systems, or network theory 
    • Applied or computational mathematics 
    • Machine learning or probabilistic modeling 
    • Quantum computing or advanced optimization techniques 
    • Topological data analysis or geometric data methods 
  • Prior research, publications, or project work demonstrating advanced quantitative modeling 
What You’ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics 
  • Opportunity to apply cutting-edge academic methods in a production environment 
  • Collaboration with a highly quantitative, cross-disciplinary team 
  • Experience working with large-scale financial data and modern analytics infrastructure 
  • Mentorship and potential pathway to full-time quantitative roles 
Duration & Compensation
  • Internship: Summer 2026, with potential to extend 
  • Paid internship (competitive, based on experience and location)