Network Operations Research Scientist

GSquared Group; Atlanta (GA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

GSquared Group is seeking an Operations Research Scientist to develop advanced optimization, ML, and decision-support systems that boost operational efficiency and reduce costs. The role combines disruption management, real-time optimization, and scalable analytics.

The candidate will collaborate with engineers and leaders to transform data into actionable insights, deploy production models, and continuously improve resilience and performance in a hybrid Atlanta setting.

Qualifications

• 3–5 years of experience developing decision-support systems.
• 3–5 years supporting disruption management and resiliency initiatives.
• 3–5 years designing optimization models for large-scale planning and real-time decisions.
• 3–5 years applying ML/optimization for autonomous decision-making.

Responsibilities

• Develop real-time, tactical, and strategic disruption management and recovery models.
• Provide production support and enhancements for existing OR applications.
• Design, develop, and implement advanced optimization, simulation, and ML models in Python, Java, or C++.
• Evaluate data quality and address gaps and issues.
• Collaborate with business partners to identify operational improvements.
• Integrate analytical models into production environments.
• Provide technical leadership and support team development.
• Communicate findings and impacts to stakeholders and senior leadership.
• Lead initiatives to improve resilience and recovery during disruptions.
• Drive continuous improvement of optimization models for scalability and efficiency.

Skills

Optimization techniques

Reinforcement learning

Machine learning

Python

SQL

Education

Master's or PhD in OR/Math/CS/Data Science

Tools

Gurobi/CPLEX

Python libraries (NumPy/SciPy)

Java or C++

Job description

Length: 6+ month contract with potential extensions

Location: Hybrid Atlanta (2 days/week onsite)

W2 or C2C

Position Overview

We are seeking an innovative and highly analytical Operations Research Scientist to develop advanced optimization, machine learning, and decision-support solutions that improve operational efficiency, enhance reliability, and reduce costs.

This role focuses on applying operations research methodologies, reinforcement learning, artificial intelligence, and mathematical optimization to solve complex business challenges. The successful candidate will collaborate 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 one of the following fields:

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

Required Experience

• 3–5 years of experience developing and deploying decision-support systems using operations research methodologies.
• 3–5 years of experience supporting disruption management, recovery planning, 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 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.

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 disruption management and recovery models to support operational continuity and 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 support team development by fostering collaboration, open communication, professional growth, 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 operational resilience, service reliability, and recovery during planned and unplanned disruptions.
• Drive the continuous improvement of optimization models and analytical solutions to enhance scalability, efficiency, and business outcomes.