Data​/Operations Research Analyst

Required Qualifications:

• Minimum of 2 years with BS/BA; 0 years with MS/MA
• Bachelor’s degree in Data Analytics, Statistics, Mathematics, Operations Research, Industrial Engineering, Information Systems, Business Analytics, Economics, Computer Science, Engineering, Public Policy, Social Sciences, or a related analytical/research field.
• Relevant experience as a data analyst, operations research analyst, research analyst, business analyst, program analyst, customer insights analyst, performance analyst, or comparable broad-based analytical role.
• Demonstrated experience producing structured analytical products from diverse data sources, including unstructured documents and correspondence, semi-structured exports, and structured databases.
• Demonstrated track record building dashboards, scorecards, and recurring reports that are actively used by leadership or customer stakeholders to make decisions.
• Working proficiency in SQL for data extraction and shaping, plus advanced Excel; experience with at least one BI/visualization tool (Power BI, Tableau, Plotly, Looker, or equivalent).
• Working proficiency in Python or R for data wrangling, exploratory analysis, and reporting automation (pandas, plotly/matplotlib, or equivalent).
• Solid grounding in quantitative methods — descriptive statistics, trend and segmentation analysis, basic inferential statistics, and exposure to operations research techniques (forecasting, optimization, simulation, queuing, or decision analysis) — with sound judgment about when to apply them.
• Demonstrated research and analysis skills — the ability to scope a question, gather authoritative sources, evaluate credibility, synthesize across sources, and cite cleanly.
• Strong critical thinking, analytical reasoning, and problem-solving skills, including the ability to assess source reliability, reconcile conflicting inputs, and quantify uncertainty.
• Comfort working with disparate, messy, and predominantly unstructured data — and a demonstrated ability to impose structure on it without losing fidelity.
• Strong written and verbal communication skills, including the ability to brief executive and customer audiences and to produce concise, well-organized written products.
• Customer-facing presence and judgment — the ability to build trust quickly, manage sensitive information appropriately, and represent the program professionally.
• Comfort operating in fast-paced, evolving environments where tools and workflows are actively being developed and refined.
• Ability to work cross-functionally with technical teams and provide clear, prioritized feedback on platform capabilities and analytical needs.

• US Citizenship
• Ability to obtain Public Trust

• Hands-on experience with AI-enabled analytical tools, large language models, agentic AI platforms, or AI-assisted research and reporting workflows.
• Experience with prompt engineering, workflow configuration, retrieval-augmented generation, or natural language interaction with AI systems in an analytical or research context.
• Advanced dashboarding skill — including data modeling for analytics, semantic-layer design, drill-through and parameterized reporting, and dashboard performance tuning.
• Experience with operations research and statistical modeling techniques beyond the basics — regression, time-series forecasting, clustering/segmentation, A/B or quasi-experimental analysis, linear/integer programming, discrete-event simulation, queuing models, Monte Carlo methods, or applied machine learning.
• Familiarity with structured analytical techniques, decision-analysis frameworks, KPI design, OKR reporting, or program performance management methodologies.
• Experience with qualitative research methods — stakeholder interviews, document analysis, thematic coding, process and workflow analysis — alongside quantitative work.
• Experience in domains beyond intelligence — such as commercial operations, federal civilian programs, healthcare, financial services, supply chain, customer experience, or engineering program management — where analytical rigor and customer trust are equally critical.
• Familiarity with data warehousing concepts, dimensional…