Senior Principal Data Scientist

Type of Requisition: Regular Clearance Level Must Currently Possess: Top Secret/SCI Clearance Level Must Be Able to Obtain: Top Secret SCI + Polygraph Public Trust/Other Required: None Job Family: Data Science and Data Engineering Job Qualifications: Skills: Data Analytics, Mathematics Modeling, Python (Programming Language), Statistical Analysis, Structured Query Language (SQL) Certifications: None Experience: 10 + years of related experience US Citizenship Required: Yes Job Description: YOUR IMPACT   Own your opportunity to work with the largest government agency in the nation. Make an impact by advancing the Department of   War ’s mission to keep our country safe and secure.   OUR COMPANY   Iron   EagleX   (IEX), a wholly owned subsidiary of General Dynamics Information Technology   (GDIT) , delivers agile IT and Intelligence solutions. Combining small-team flexibility with global scale, IEX leverages emerging technologies to provide innovative, user-focused solutions that empower organizations and end users to   operate   smarter, faster, and more securely in dynamic environments.    JOB DESCRIPTION   Iron   EagleX   is seeking a Senior   Principal   Data Scientist to join our dynamic team in Crystal City, VA. This role creates and delivers innovative analytic solutions as a member of a fast-paced, multidisciplinary team.   You   will work directly with large, complex, and disparate datasets to develop practical analytic methods,   identify   meaningful patterns and relationships, and translate technical findings into capabilities and insights that support critical customer requirements.   M EANINGFUL WORK AND PERSONAL IMPACT   As a Senior   Principal  Data Scientist, you will quickly turn large and complex datasets into clear, actionable insights for critical customer requirements. You will work closely with analysts, software developers, and other technical specialists to solve difficult data problems, develop new analytic approaches, and transition successful methods from exploratory analysis into repeatable and operational capabilities.   JOB DUTIES   (INCLUDE BUT ARE NOT LIMITED TO)   Implement structured, repeatable data analysis across large, disparate datasets to surface patterns, trends, anomalies, relationships, and other signals in support of mission and analytic needs.   Explore and characterize unfamiliar datasets, including assessing data quality, completeness, distributions, relationships, and limitations to determine   appropriate analytic   approaches and   identify   potentially useful signals.   Develop,   maintain , and improve analytic tooling such as queries, scripts, notebooks, lightweight services, and reusable code components to automate recurring workflows and enable rapid analysis.   Develop and evaluate applied statistical, machine learning, and algorithmic approaches for problems such as classification, clustering, anomaly detection, similarity analysis, prioritization, entity resolution, relationship discovery, and predictive analysis.   Establish   appropriate validation   methods, benchmarks, scoring approaches, thresholds, and measures of confidence to evaluate analytic performance and clearly communicate the strengths and limitations of analytic results.   Build and enhance interactive analytic dashboards and lightweight GUIs, such as   Streamlit   applications, that support data exploration, linkage review, analyst workflows, model or algorithm evaluation, and generation of structured outputs.   Create analyst-ready products, including tables, visualizations, summaries, briefings, and structured exports, that translate technical findings into clear, decision-oriented insights.   Work with analysts, engineers, and technical staff to convert ad hoc analyses and successful prototypes into reusable pipelines, standardized methodologies, documented workflows, and maintainable analytic capabilities.   Support the integration, testing, and refinement of analytic methods in operational environments, ensuring outputs are reproducible, explainable, and usable by both technical and non-technical stakeholders.   Document analytic assumptions, methodologies, data transformations, validation approaches, and known limitations to promote reproducibility, peer review, and continued improvement of analytic capabilities.   REQUIRED SKILLS:   Strong Python skills for building practical analytic solutions, including data processing, automation, exploratory analysis, visualization, algorithm development, and reproducible scripts or notebooks using libraries such as pandas, NumPy, SciPy, scikit-learn, or similar tools.   Strong SQL and hands-on experience working directly with large datasets in modern data platforms such as Trino, PostgreSQL, Hive, OpenSearch, or Elasticsearch; experience working with distributed query or large-scale data environments is strongly preferred.   Experience applying statistical, machine learning, or algorithmic techniques to real-world datasets, with the ability to select   appropriate approaches   based on the characteristics of the data and the operational problem rather than relying solely on predefined models or techniques.   Experience designing and implementing repeatable analytic workflows, including source triage, exploratory data analysis, data profiling, quality checks, transformation logic, feature development, validation, documentation, and reusable code patterns.   Practical experience developing analytic methods for entity resolution, relationship discovery,   graph   and relational analysis, including implementation of scoring, thresholds, validation checks, and measures of analytic confidence.   Experience evaluating analytic methods using   appropriate metrics , baselines, test datasets, sensitivity analysis, or other validation approaches and   identifying   sources of error, uncertainty, or degraded performance.   Experien