Data/Operations Research Analyst
$104,000–$166,000 year
On-siteHome, Washington, United States
Home, Washington, United StatesOn-siteFull Time$104,000–$166,000 yearEnterprise
Full TimeEnterprise
Job Summary
Embed within an assigned program to develop deep customer intimacy and translate that understanding into data-driven products that drive action. Conduct broad-based data and operations research analysis across program-generated data, leveraging the customer-deployed Generative AI Platform, agentic workflows, and AI-assisted analytical tools to produce timely, defensible findings. Acquire, profile, clean, and integrate data from disparate sources, then apply quantitative methods and primary research to answer program questions. Synthesize information from unstructured sources into clear narratives, design dashboards and recurring reports for leadership, and translate ambiguous customer questions into structured analytical plans. Evaluate AI-generated outputs for accuracy, develop reusable templates and prompt libraries, anticipate emerging trends proactively, and provide continuous feedback to engineering teams on platform usability and workflow gaps.
Required Qualifications
- Minimum of 8 years with BS/BA
- Minimum of 6 years with MS/MA
- Minimum of 3 years with PhD
- Minimum of a 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
- 5–10 years of 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
- Working proficiency in 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)
- 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
- Location: Columbus, Ohio — candidates must currently reside in the area or be willing to relocate
Desired Qualifications
- 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 modeling, or modern data stack tooling (dbt, Snowflake, Databricks, or equivalent)
- Background in evaluating or adopting new analytical technologies, including participation in pilot programs, technology transitions, or capability assessments
- Experience developing analytical SOPs, methodology guides, training materials, dashboard standards, or report style guides
- Familiarity with knowledge management, data curation, taxonomy/ontology work, or information organization in support of analytical and research workflows
- Experience embedding with a customer or program team for an extended period and being recognized as a trusted advisor rather than an external contributor
- Exposure to Agile delivery, sprint-based reporting cadences, and cross-functional team collaboration
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