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Dynamic Stochastic Optimization Jobs (NOW HIRING)

Stellantis

Data Scientist

Auburn Hills, MI · On-site

Dynamic Programming * Simulation * Stochastic Optimization * Tree-based models (Random Forest, XGBoost, LightGBM) * Neural networks * Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic ...

Bank of America

Data Scientist I

Charlotte, NC · On-site

You will work across teams to solve problems in recommendation, stochastic optimization, and time ... Are energized by the high stakes and intensity of dynamic environments and ready to dive in ...

Bank of America

Data Scientist I

New York, NY · On-site

You will work across teams to solve problems in recommendation, stochastic optimization, and time ... Are energized by the high stakes and intensity of dynamic environments and ready to dive in ...

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Dynamic Stochastic Optimization information

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$16K

$55.8K

$102K

How much do dynamic stochastic optimization jobs pay per year?

As of Sep 9, 2026, the average yearly pay for dynamic stochastic optimization in the United States is $55,794.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,000.00 and $72,500.00 per year, depending on experience, location, and employer.

What is dynamic stochastic optimization?

Dynamic stochastic optimization is a mathematical approach used to make optimal decisions over time in situations where outcomes are uncertain and may change. It combines dynamic programming (making a sequence of decisions) with stochastic modeling (accounting for randomness). This method is widely used in fields like finance, engineering, and operations research to solve problems such as investment planning, resource allocation, and supply chain management. By modeling uncertainties and the evolution of systems over time, it helps decision-makers find strategies that maximize expected performance or minimize risk.

What are the key skills and qualifications needed to thrive as a dynamic stochastic optimization specialist?

To thrive as a Dynamic Stochastic Optimization Specialist, you need a solid background in mathematics, statistics, operations research, and computer science, typically supported by an advanced degree in a quantitative field. Proficiency with programming languages (such as Python, MATLAB, or R), optimization software (like Gurobi or CPLEX), and familiarity with simulation tools are essential. Strong analytical thinking, creative problem-solving, and effective communication skills help you translate complex models into actionable insights. These competencies are crucial for designing, implementing, and communicating robust optimization models that drive decision-making in uncertain and dynamic environments.

What are the typical collaboration opportunities for professionals working in dynamic stochastic optimization roles?

Professionals in Dynamic Stochastic Optimization often collaborate closely with data scientists, software engineers, and domain experts to develop and implement optimization models that adapt to uncertainty over time. They may also work with decision-makers or operational teams to translate complex mathematical solutions into actionable strategies. This interdisciplinary collaboration helps ensure that the models are both mathematically sound and practical for real-world applications, such as supply chain management, finance, or energy systems.

What is the difference between Dynamic Stochastic Optimization vs Data Analyst?

AspectDynamic Stochastic OptimizationData Analyst
Required CredentialsAdvanced degrees in Operations Research, Mathematics, or related fieldsBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentQuantitative teams, research labs, or analytics departmentsBusiness units, marketing, finance, or operations teams
Industry UsageSupply chain, finance, energy, and logisticsMarketing, finance, healthcare, and retail sectors
Search & Comparison IntentUnderstanding optimization techniques for decision-making under uncertaintyAnalyzing data to inform business decisions

Dynamic Stochastic Optimization focuses on developing models to make optimal decisions in uncertain environments, often requiring advanced mathematical skills. Data Analysts interpret and analyze data to support business strategies. While both roles involve data, their applications, skills, and industries differ significantly.

What other helpful pages are available for Dynamic Stochastic Optimization?

Other pages related to Dynamic Stochastic Optimization:

Infographic showing various Dynamic Stochastic Optimization job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $55,794 per year, or $26.8 per hour.

Data Scientist

Auburn Hills, MI • On-site

Stellantis
IT Services
Learn more about Stellantisexternal

Full-time

Medical, Dental, Vision, Retirement, PTO

This job post has expired 1 day ago. Applications are no longer accepted.

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 131 frontline employees who took The Breakroom Quiz

13th of 45 rated automakers

Job description

Job Overview: 

The Commercial Analytics group is looking for a Data Scientist to join our team. Your mission is to build and scale trusted data science products that power commercial recommendations while promoting data science best practices, actionable outputs and a high bar for model quality and reliability.  

Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases. Ideal candidates are self-motivated, inquisitive and creative, with a strong desire to solve real-world problems using data. 

In this role, you will: 

  • Collaborate with business stakeholders to identify high-impact opportunities for statistical and machine learning use cases. 
  • Develop defensible, well-documented methodologies that stand up to executive scrutiny and support strategic decision-making. 
  • Communicate complex results clearly to both technical and non-technical audiences. 
  • Partner with data engineers to define and source relevant data features for modeling as well as drive adoption and a deep understanding of proper data usage. 
  • Develop and validate models using techniques such as regression, optimization (linear programming, dynamic programming, etc.), gradient boosting, dimensionality reduction and neural networks. 
  • Communicate findings and recommendations to non-technical audiences through clear visualizations and storytelling. 
  • Contribute to the maintenance of models in production environments, ensuring scalability and performance. 
  • Conduct peer code reviews and support best practices in model development and deployment. 
  • Collaborate with both external and internal resources to support business requirements and key KPI measurement  

Basic Qualifications: 

  • Bachelor’s degree in a quantitative discipline (e.g., Operations Research, Applied Mathematics, Optimization, Data Science or other quantitative field) 
  • Automotive experience 
  • Minimum of 5 years of experience in operations research, systems engineering, data science, or a related field 
  • Proficiency in Python and SQL 
  • Hands-on experience with big data and cloud platforms such as Databricks, Snowflake or Spark 
  • Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines 
  • Strong grasp of mathematical concepts like: 
    • Regression (linear, logistic) 
    • Linear & Non-Linear Programming 
    • Network Flow Models 
    • Dynamic Programming 
    • Simulation 
    • Stochastic Optimization 
    • Tree-based models (Random Forest, XGBoost, LightGBM) 
    • Neural networks 
    • Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping) 
  • Experience communicating optimization tradeoff and recommendations to executive stakeholders 

Our Benefits — Designed with You in Mind

Comprehensive Health & Well-being Coverage
From your very first day, you’ll have access to medical, dental, vision, and prescription drug coverage — ensuring you and your family stay healthy and protected.

Generous Paid Time Off
We believe in work-life balance. That’s why we offer: 17+ paid holidays, including shut-down from December 24th through New Years Day every year. Vacation, float & wellbeing days, sick time and fully paid parental leave when your family needs you most.

Competitive Retirement Savings Plans
We help you plan for the future with:

  • An employer match on contributions to your 401k, Roth, and Catch-Up plans
  • An employer contribution, even if you don’t contribute

Income Protection & Insurance Options
Benefit from included and optional disability, life, and other insurance programs — because your peace of mind matters.

Company Vehicle Lease Program
Eligible employees and their immediate families can enjoy company vehicle lease options with included insurance, maintenance, and unlimited mileage. Plus, take advantage of exclusive discounts on Stellantis products.

Family Building Benefit
We proudly support all paths to parenthood- including fertility and infertility treatments, adoption services, and gestational surrogacy.

Support for Your Growth and Giving Back
We believe in investing in your future and your passions:

  • Tuition reimbursement
  • Student loan refinancing programs
  • 18 paid volunteer hours each year to make a difference in your community

And so much more!
When you join us, you’re not just building a career — you’re joining a company that supports you, inside and outside of work.

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