Data Scientist - Operational Analytics

Peraton•Herndon, VA

About The Position

Peraton Labs is seeking a Data Scientist to support the BNATCS CTO Advanced Analytics and Digital Command Center teams by transforming complex workstream, program, schedule, requirements, and operational data into trusted metrics, analytical insights, and decision-support products. This role will support the analytics foundation behind the Digital Command Center (DCC) by helping interpret, model, visualize, and operationalize complex workstream data. You will work closely with CTO leadership and mission stakeholders to transform raw and evolving program data into actionable insights, dashboards, metrics, and analytics products that support FAA modernization objectives. The ideal candidate for this role brings strong analytical judgment, cognitive flexibility, hands-on comfort with data, and the ability to understand both the technical structure of data and the operational meaning behind it. Candidates for this role should be able to analyze, reason, and communicate well enough to contribute to analytics workflows, dashboard logic, data validation, and integration activities supporting the consolidated data environment that feeds the program’s agentic AI engine. Experience with FAA, NAS, Aviation, air traffic operations, transportation, safety-critical systems, or large-scale modernization data is highly relevant to this position.

Requirements

5+ years of experience with a BS/BA, 3+ years with a MS/MA, or a PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, Operations Research, Information Systems, Analytics, or a related technical discipline
Experience analyzing complex structured, semi-structured, operational, program, or business datasets to support decision-making
Proficiency with Python, R, SQL, or similar analytical tools, with the ability to develop notebooks, scripts, queries, and reusable analysis artifacts
Experience with exploratory data analysis, data cleaning, statistical analysis, trend analysis, anomaly detection, forecasting, classification, clustering, or predictive modeling
Experience defining or supporting metrics, KPIs, dashboards, reports, or decision-support products
Ability to assess data quality, completeness, consistency, and analytical usefulness
Ability to translate ambiguous operational and stakeholder questions into structured analyses and measurable outputs
Ability to vet and validate analytic products developed with Agentic AI
Strong communication skills with the ability to communicate clearly with a variety of technical and program stakeholders
US Citizenship with ability to obtain/maintain an FAA Public Trust

Nice To Haves

Experience supporting FAA, aviation, NAS, air traffic management, transportation, defense, logistics, infrastructure, command center, risk, or other complex operational environments
Experience with cloud-based data environments, especially AWS, Glue, Athena, Redshift, SageMaker, Databricks, Snowflake, or similar
Experience working with data lakes, data warehouses, lakehouse environments, or multi-source data integration environments
Experience with dashboarding or visualization tools such as Tableau, Power BI, Grafana, Plotly, Dash, Streamlit, Superset, Qlik, or similar platforms
Familiarity with LLMs, agentic AI systems, retrieval-augmented generation (RAG), prompt/context engineering, AI-enabled workflows, or model/output evaluation
Experience supporting human-in-the-loop analytics, AI output validation, data quality frameworks, or decision-support governance
Experience working with requirements data, schedule data, risk data, dependency data, operational performance data, or program execution data
Familiarity with APIs, JSON, Parquet, data pipelines, ETL/ELT concepts, metadata, lineage, or data governance concepts
Experience contributing lightweight code to dashboards, data workflows, APIs, or analytics applications

Responsibilities

Analyze workstream, requirements, IMS, schedule, risk, operational, and program data to identify trends, dependencies, gaps, anomalies, and decision-relevant insights
Define operational metrics, KPIs, analytical logic, thresholds, and decision-support views for Digital Command Center dashboards and advanced analytics products
Assess the quality, completeness, consistency, and usability of data made available through enterprise data repositories
Partner with data architects and data engineers to identify data gaps, transformation needs, metadata requirements, and analytics-readiness issues
Support ai-enabled analytics workflows by helping define trusted data inputs, context, validation criteria, and human-in-the-loop review considerations
Develop notebooks, scripts, SQL queries, statistical analyses, models, and reusable analytical artifacts to support data exploration and decision-making
Translate ambiguous operational questions into structured analysis, measurable metrics, and clear recommendations
Communicate assumptions, limitations, findings, and implications to technical teams, program leadership, and mission stakeholders