Operations Research Systems Analyst information
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$43.5K - $51.9K
3% of jobs
$51.9K - $60.3K
14% of jobs
$60.3K - $68.7K
0% of jobs
$68.7K - $77.1K
1% of jobs
$77.1K - $85.5K
1% of jobs
$95.8K is the 25th percentile. Wages below this are outliers.
$94K - $102.4K
21% of jobs
$102.4K - $110.8K
0% of jobs
The median wage is $112K / yr.
$110.8K - $119.2K
56% of jobs
$119.2K - $127.6K
0% of jobs
$127.6K - $136K
2% of jobs
How much do operations research systems analyst jobs pay per year?
As of Aug 19, 2026, the average yearly pay for operations research systems analyst in the United States is $102,462.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $116,500.00 per year, depending on experience, location, and employer.
Operations Research Systems Analysts are professionals who use advanced mathematical and analytical methods to help organizations solve complex problems and make better decisions. They collect and analyze data, develop mathematical models, and use statistical analysis to identify trends or inefficiencies in business operations. These analysts often work in industries such as logistics, healthcare, manufacturing, and government to optimize processes and improve organizational outcomes. Their work supports strategic planning, resource allocation, and operational efficiency.
To thrive as an Operations Research Systems Analyst, you need strong analytical, mathematical, and problem-solving skills, typically supported by a degree in operations research, mathematics, engineering, or a related field. Familiarity with optimization software, statistical analysis tools (such as MATLAB, R, or Python), and simulation modeling systems is commonly required. Excellent communication, teamwork, and critical thinking skills help analysts convey complex findings and collaborate on solutions. These competencies are crucial for developing effective strategies that improve organizational efficiency and decision-making.
Operations Research Systems Analysts often work closely with professionals from IT, engineering, finance, and management to develop and apply mathematical models for organizational improvement. They facilitate communication between technical and non-technical stakeholders, ensuring that complex data analyses and recommendations are clearly understood and actionable. Collaboration may involve leading workshops, participating in project meetings, and translating analytical findings into practical strategies. This teamwork is essential for successfully implementing data-driven solutions that align with organizational goals.
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