Operations Research Scientist

Revolution Technologies; Atlanta (GA); On-site; USD 110,000 - 150,000; Full time

Job summary

Client Corporation seeks an innovative Operations Research Scientist to develop optimization, ML, and decision-support solutions that improve network performance, efficiency, and reliability. You will work with stakeholders, software engineers, and leaders to transform data into scalable decision-support systems for transportation and logistics challenges.

The role emphasizes cutting-edge optimization, reinforcement learning, and AI methods to solve complex problems, with collaboration across

Qualifications

• Master's or PhD in quantitative field including OR/Math/CS
• 3-5 years deploying decision-support systems
• Experience with network disruption and resiliency
• Experience with optimization for large-scale planning
• Experience applying ML/AI to optimization problems
• Software development practices and production deployment

Responsibilities

• Develop real-time, tactical, and strategic network disruption models for railroad operations.
• Provide production support and continuous enhancements for OR applications.
• Design and implement optimization, simulation, and ML models in Python/Java/C++.
• Evaluate data quality and close data gaps for modeling.
• Collaborate with business partners to identify operational improvements.
• Integrate analytical models into production environments.
• Provide technical leadership and mentorship to the team.
• Communicate findings and business impact to stakeholders.
• Lead resilience and recovery initiatives during disruptions.

Skills

Operations research

Optimization theory

Reinforcement learning

Machine learning

Python programming

Java or C++

SQL & databases

Model deployment

Education

Master's in Operations Research

Master's in Applied Mathematics

Master's in Statistics

Master's in Computer Science

Master's in Information Systems

Master's in Data Science

Other quantitative disciplines

Tools

Gurobi

CPLEX

Relational databases

Job description

Client Corporation is seeking an innovative and highly analytical Operations Research Scientist to develop advanced optimization, machine learning, and decision-support solutions that improve network performance, increase operational efficiency, enhance service reliability, and reduce costs.

This role leverages cutting-edge operations research methodologies, reinforcement learning, artificial intelligence, and mathematical optimization to solve complex transportation and logistics challenges. The successful candidate will work closely with business stakeholders, software engineers, and operational leaders to transform data into actionable insights and scalable decision-support systems.

Education

Master's or Ph.D. degree in:

• Operations Research
• Applied Mathematics
• Statistics
• Computer Science
• Information Systems
• Data Science
• Other related quantitative disciplines

Experience

Required:

• 3-5 years of experience developing and deploying decision-support systems using operations research methodologies.
• 3-5 years of experience supporting network recovery, disruption management, and operational resiliency initiatives.
• 3-5 years of experience designing and implementing optimization models for large-scale operational planning and real-time decision-making applications.
• 3-5 years of experience applying reinforcement learning, machine learning, and network optimization techniques to support adaptive and autonomous decision-making.
• Experience developing nonlinear optimization and AI-enhanced optimization models that improve solution quality, robustness, scalability, and explainability.
• 3-5 years of experience following software development best practices, including design, testing, deployment, and maintenance of production applications.

Preferred:

• Knowledge of railroad operations, transportation planning, and network design principles.
• Experience with train scheduling, routing, blocking, crew planning, or asset utilization optimization.
• Experience measuring, analyzing, and improving transportation service performance.
• Experience developing real-time analytics and decision-support systems for large-scale logistics or transportation networks.

Technical Skills

• Strong expertise in mathematical optimization techniques, including Linear Programming (LP), Mixed Integer Programming (MIP), and Nonlinear Optimization (NLP).
• Experience with commercial or open-source optimization solvers such as Gurobi or CPLEX.
• Experience with machine learning, predictive analytics, neural networks, deep learning, or discrete-event simulation.
• Proficiency in Python and experience with one or more additional programming languages such as Java or C++.
• Experience with model deployment and production integration.
• Strong SQL skills and experience working with large-scale relational databases.
• Experience with data quality assessment, feature engineering, and model validation.

Key Responsibilities

• Develop real-time, tactical, and strategic network disruption management and recovery models to support railroad operations and service resiliency.
• Provide production support, maintenance, and continuous enhancements for existing operations research applications.
• Design, develop, and implement advanced mathematical optimization, simulation, and machine learning models using Python, Java or C++.
• Evaluate the accuracy, completeness, and quality of data sources, and develop effective solutions to address data quality issues and gaps.
• Collaborate with business partners across multiple departments to identify opportunities for operational improvements and process optimization.
• Work closely with application development teams to integrate analytical models and decision-support tools into production environments.
• Provide technical leadership and supervise team members to achieve organizational goals by fostering collaboration, open communication, professional development, and adaptability.
• Communicate analytical findings, model recommendations, and business impacts to stakeholders and senior leadership to support data-driven decision-making.
• Lead initiatives focused on improving network resilience, service reliability, and operational recovery during planned and unplanned disruptions.

Shift Work: No

On-Call: Yes

Travel Required: Approximately one week per year