Operations Research Scientist

Operations Research Scientist

Environment: Hybrid-each Week-2 days onsite Location: Atlanta, GA 30308 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
• Industrial Engineering
• 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 (Nice to have)

• 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.