Operations Research Analysts

Formulate and apply mathematical modeling and other optimizing methods to develop and interpret information that assists management with decisionmaking, policy formulation, or other managerial functions. May collect and analyze data and develop decision support software, services, or products. May develop and supply optimal time, cost, or logistics networks for program evaluation, review, or implementation.

The occupation "Operations Research Analysts" has an automation risk of 49.0%, slightly below the base risk of 50.0%. This indicates a balanced threat from automation: a significant part of the role can potentially be automated, while some core tasks remain resistant. Operations research relies heavily on analytical and quantitative skills, which have recently seen rapid automation progress due to advancements in artificial intelligence, machine learning, and statistical software. Many software tools now automate complex model creation, data crunching, and even some aspects of report generation, directly impacting how these professionals work. However, the full automation of the occupation is limited by the need for domain-specific expertise and critical thinking in interpreting results and designing solutions. The most automatable tasks for operations research analysts involve standardized procedures and computational work. For example, presenting results of mathematical modeling and data analysis to management or end users can often be performed by automated reporting tools. Similarly, processes like defining data requirements, gathering and validating information, and using statistical tests are increasingly facilitated by automated data pipelines and AI-powered validation algorithms. Validation and testing of models, while once labor-intensive, are now aided by robust software solutions that can check the adequacy of multiple models in parallel and suggest reformulations, accelerating the workflow and reducing the need for manual intervention. Despite these trends, several key responsibilities remain resistant to automation, preserving the need for human analysts. Reviewing research literature requires critical reading and synthesis skills that current AI systems still struggle to perform reliably. The development of new business methods and procedures—including the design of accounting, logistics, and production systems—involves contextual judgment, creativity, and nuanced understanding of an organization that algorithms find challenging to replicate. Additionally, educating staff on the use of mathematical models is inherently a human-centered task, involving communication, adaptation, and instructional skills. These tasks benefit from bottleneck skills like originality, with measured skill levels of 3.6% and 4.0%, highlighting the value of creative, non-routine thinking in maintaining a human edge in this occupation.