Senior Principal Data Scientist

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.

MEANINGFUL 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.

• Experience integrating structured, semi-structured, and text-based data, including extracting key fields and signals, normalizing data across sources, resolving inconsistencies, and combining schemas to support downstream analysis and tooling.

• Ability to independently investigate complex or poorly defined analytic problems, rapidly become familiar with unfamiliar datasets, formulate testable approaches, and iteratively refine solutions based on results and stakeholder feedback.

• Ability to translate technical analysis into usable outputs for analysts and decisionmakers, including clear visualizations, concise analytic narratives, dashboards, structured deliverables, and explanations of analytic confidence and limitations.

• Strong communication and collaboration skills with the ability to work effectively across multidisciplinary teams that include analysts, data scientists, data engineers, software engineers, and mission stakeholders.

DESIRED SKILLS

• Experience with applied machine learning techniques such as classification, clustering, dimensionality reduction, anomaly detection, ranking, similarity analysis, natural language processing, or time-series analysis.

• Experience working with graph-based data and analytics, including knowledge graphs, network analysis, graph databases, or techniques for identifying relationships and communities across interconnected data.

• Experience working with geospatial, temporal, or other specialized data types and incorporating spatial or time-dependent relationships into analytic workflows.

• Familiarity with modern AI and large language model capabilities, including using LLMs for information extraction, classification, summarization, entity identification, or other components of broader data science and analytic workflows.

• Experience developing or integrating APIs and lightweight Python services, such as FastAPI, to expose analytic methods or data science capabilities to downstream applications and users.

• Familiarity with deploying applications and analytic capabilities in Dockerized environments and utilizing CI/CD pipelines to support repeatable, secure, and maintainable software delivery.

• Familiarity with Git best practices, including branching strategies, pull requests, code reviews, merge conflict resolution, and maintaining clean, well-documented repositories.

• Experience integrating React-based frontends with RESTful APIs and Python-based backend services.

• Familiarity with large-scale data processing technologies such as Apache Iceberg, Spark, object storage platforms, or similar modern data architectures.

• Experience working in multidisciplinary environments where data science capabilities must be transitioned from exploratory research or prototyping into reliable, maintainable operational tools.

WHAT YOU’LL NEED TO SUCCEED

• Clearance: Current TS/SCI Clearance with current or willingness to obtain CI polygraph

• Experience: 10+ years of related experience

• Education: Bachelor's degree in Computer Science, Statistics, Engineering, or a related field (or equivalent experience). Advanced degrees are a plus.

• Role requirements: This position is onsite in Crystal City, VA and requires travel for 60-90 days to CONUS sites each year.

• Due to US Government Contract Requirements, only US Citizens are eligible for this role

GDIT IS YOUR PLACE
At GDIT, the mission is our purpose, and our people are at the center of everything we do.

• Growth: AI-powered career tool that identifies career steps and learning opportunities

• Support: An internal mobility team focused on helping you achieve your career goals

• Rewards: Comprehensive benefits and wellness packages, 401K with company match, competitive pay and paid time off

• Community: Award-winning culture of innovation and a military-friendly workplace

OWN YOUR OPPORTUNITY
Explore a career at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your passion for the mission and delivering results. #iexjobs

Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans