Data/Operations Research Analyst

Peraton is hiring a Data/Operations Research Analyst to embed with a program and turn disparate structured and unstructured information into decision-ready analysis. The analyst will use a customer-deployed Generative AI Platform to produce dashboards, reports, and research products, while providing feedback to improve platform workflows and capabilities.

Responsibilities:

• Embed within an assigned program to develop deep customer intimacy — understanding the program’s mission, stakeholders, data landscape, decision cadences, and reporting obligations — and translate that understanding into data-driven products that drive action
• Conduct broad-based data and operations research analysis across whatever data the program generates or consumes, leveraging the customer-deployed Generative AI Platform, agentic workflows, structured retrieval, and AI-assisted analytical tools to produce timely, defensible findings
• Serve as a power user of the Generative AI Platform — deeply learning its capabilities, identifying optimal workflows, and pushing the boundaries of AI-augmented data analysis and operations research
• Acquire, profile, clean, and integrate data from disparate sources — flat files, exports, APIs, databases, knowledge bases, and unstructured content — establishing the data foundations on which downstream analysis depends
• Apply quantitative methods (descriptive statistics, trend and cohort analysis, segmentation, correlation, basic inferential techniques, and where appropriate forecasting, optimization, or simulation) to answer program and customer questions
• Conduct primary and secondary research and operations analysis — literature scans, document review, stakeholder interviews, process and workflow analysis, performance studies, market and competitor scans, policy or regulatory review — and synthesize findings into clear, well-cited products
• Synthesize information from disparate, predominantly unstructured sources (documents, email threads, meeting transcripts, customer correspondence, tickets, logs, knowledge bases, structured exports) into clear, well-sourced narratives and quantitative summaries
• Design, build, and maintain dashboards, scorecards, and recurring reports that give program leadership and the customer a single, trustworthy view of status, risk, performance, and opportunity — with the analytical depth to back every number shown
• Translate ambiguous customer questions into structured analytical plans, retrieval patterns, prompt strategies, and multi-step workflows that produce repeatable, auditable answers
• Evaluate and validate AI-generated outputs, applying analytical rigor and program-specific judgment to ensure accuracy, source traceability, and methodological soundness before any product reaches the customer
• Develop reusable analytical templates, prompt libraries, dashboard components, briefing formats, and report packages that improve speed and consistency across the program and can be lifted to other programs
• Anticipate customer information needs — surfacing emerging trends, exceptions, anomalies, and risks proactively rather than waiting to be tasked
• Provide continuous, well-articulated feedback to engineering and product teams on platform usability, workflow gaps, integration needs, and reporting features that would unlock additional program value
• Support demonstrations, pilot use cases, and proof-of-concept analyses that show the platform’s value to program stakeholders and customer leadership
• Collaborate across the program team — engineers, architects, project managers, and customer staff — to identify high-value analytical use cases and prioritize platform enhancements
• Document data sources, definitions, methodologies, and limitations so analytical products are reproducible and the capability is transferable
• Support training, onboarding, and enablement of additional analysts and program staff by sharing expertise, dashboards, report templates, and lessons learned

Requirements:

• 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 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