Front Office Data Analyst Research Analyst Jobs

• 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.

Peraton is seeking a Data/Operations Research Analyst to serve as a program-embedded power user and customer-facing operator of a customer-deployed Generative AI Platform. This role is purpose-built for customer intimacy: the analyst is uniquely aligned to a single program, becomes deeply fluent in its data, stakeholders, and operating rhythms, and converts that proximity into clear, defensible insight. The position is intentionally broad-based — applying data analysis and operations research tradecraft that is domain-agnostic and applicable wherever decisions must be made from imperfect, disparate, and largely unstructured information (operations, program management, customer experience, supply chain, finance, compliance, engineering performance, and beyond).

This individual blends the quantitative rigor of a data analyst with the modeling and decision-analysis discipline of an operations research analyst. They will leverage the full breadth of the Generative AI Platform — including agentic workflows, natural language interfaces, retrieval over enterprise content, structured analysis, and automated reporting — to fuse heterogeneous inputs (documents, emails, tickets, logs, spreadsheets, transcripts, databases, briefings, and ad-hoc artifacts) into coherent, decision-ready products. The analyst’s defining superpower is the ability to translate that synthesis into compelling dashboards, recurring reports, and on-demand analyses that program leadership and the customer actually use to run the work.

As a power user, this analyst will also help shape platform development by providing direct, mission-grounded feedback on usability, capability gaps, workflow effectiveness, and reporting needs — ensuring the platform evolves in ways that demonstrably improve program outcomes and customer satisfaction.

Columbus, Ohio — candidates must currently reside in the area or be willing to relocate.

• 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.