Senior Data Scientist - Operational Analytics

Peraton•Bowie, MD

4d•$104,000 - $166,000

About The Position

Peraton Labs is seeking a Senior Data Scientist to support the BNATCS CTO Advanced Analytics and Digital Command Center teams. The role transforms complex workstream, program, schedule, requirements, and operational data into trusted metrics, analytical insights, and decision-support products. It supports the analytics foundation for the Digital Command Center by interpreting, modeling, visualizing, and operationalizing complex workstream data in support of FAA modernization objectives. The successful candidate will apply analytical judgment and hands-on data skills while considering both the technical structure of data and its operational meaning. The role contributes to analytics workflows, dashboard logic, data validation, and integration activities for the consolidated data environment that feeds the program’s agentic AI engine. Relevant domain experience includes FAA, NAS, aviation, air traffic operations, transportation, safety-critical systems, and large-scale modernization data.

Requirements

At least 8 years of experience with a BS/BA, 6 years with an MS/MA, or 3 years with 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, including developing 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.
U.S. citizenship and ability to obtain an FAA Public Trust clearance.

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 in 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 for 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.