{"id":587461,"url":"https://alion.io/job/general-dynamics-information-technology-senior-principal-data-scientist","title":"Senior Principal Data Scientist","company":{"id":4418,"name":"General Dynamics Information Technology","domain":"gdit.com","url":"https://alion.io/company/gdit","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":95,"open_postings":39,"ghost_share":0,"stale_share":0.41,"repost_share":0,"time_to_fill_p50_days":10,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":187000,"max":253000,"currency":"USD","period":"year","gross":null,"usd_annual":253000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ElasticSearch","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"OpenSearch","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Trino","optional":false},{"name":"Apache Iceberg","optional":true},{"name":"Git","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Spark","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-09-09T00:00:00Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-24T08:05:52Z","board_verified":false,"closed_at":null,"days_open":47,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":47},"description":"Type of Requisition:\nRegularClearance Level Must Currently Possess:\nTop Secret/SCIClearance Level Must Be Able to Obtain:\nTop Secret SCI + PolygraphPublic Trust/Other Required:\nNoneJob Family:\nData Science and Data EngineeringJob Qualifications:\nSkills:\nData Analytics, Mathematics Modeling, Python (Programming Language), Statistical Analysis, Structured Query Language (SQL)Certifications:\nNoneExperience:\n10 + years of related experienceUS Citizenship Required:\nYesJob Description:\nYOUR IMPACT\nOwn your opportunity to work with the largest government agency in the nation. Make an impact by advancing the Department ofWar’s mission to keep our country safe and secure.\nOUR COMPANY\nIronEagleX(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 tooperatesmarter, faster, and more securely in dynamic environments.\nJOB DESCRIPTION\nIronEagleXis seeking a SeniorPrincipalData 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.Youwill work directly with large, complex, and disparate datasets to develop practical analytic methods,identifymeaningful patterns and relationships, and translate technical findings into capabilities and insights that support critical customer requirements.\nMEANINGFUL WORK AND PERSONAL IMPACT\nAs a SeniorPrincipalData 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.\nJOB DUTIES (INCLUDE BUT ARE NOT LIMITED TO)\nImplement structured, repeatable data analysis across large, disparate datasets to surface patterns, trends, anomalies, relationships, and other signals in support of mission and analytic needs.\n\nExplore and characterize unfamiliar datasets, including assessing data quality, completeness, distributions, relationships, and limitations to determineappropriate analyticapproaches andidentifypotentially useful signals.\n\nDevelop,maintain, and improve analytic tooling such as queries, scripts, notebooks, lightweight services, and reusable code components to automate recurring workflows and enable rapid analysis.\n\nDevelop 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.\n\nEstablishappropriate validationmethods, benchmarks, scoring approaches, thresholds, and measures of confidence to evaluate analytic performance and clearly communicate the strengths and limitations of analytic results.\n\nBuild and enhance interactive analytic dashboards and lightweight GUIs, such asStreamlitapplications, that support data exploration, linkage review, analyst workflows, model or algorithm evaluation, and generation of structured outputs.\n\nCreate analyst-ready products, including tables, visualizations, summaries, briefings, and structured exports, that translate technical findings into clear, decision-oriented insights.\n\nWork 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.\n\nSupport 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.\n\nDocument analytic assumptions, methodologies, data transformations, validation approaches, and known limitations to promote reproducibility, peer review, and continued improvement of analytic capabilities.\n\nREQUIRED SKILLS:\nStrong 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.\n\nStrong 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.\n\nExperience applying statistical, machine learning, or algorithmic techniques to real-world datasets, with the ability to selectappropriate approachesbased on the characteristics of the data and the operational problem rather than relying solely on predefined models or techniques.\n\nExperience 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.\n\nPractical experience developing analytic methods for entity resolution, relationship discovery,graphand relational analysis, including implementation of scoring, thresholds, validation checks, and measures of analytic confidence.\n\nExperience evaluating analytic methods usingappropriate metrics, baselines, test datasets, sensitivity analysis, or other validation approaches andidentifyingsources of error, uncertainty, or degraded performance.\n\nExperience 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.\n\nAbility 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.\n\nAbility to translate technical analysis into usable outputs for analysts anddecisionmakers, including clear visualizations, concise analytic narratives, dashboards, structured deliverables, and explanations of analytic confidence and limitations.\n\nStrong communicationand collaboration skills with the ability to work effectively across multidisciplinary teams that include analysts, data scientists, data engineers, software engineers, and mission stakeholders.\n\nDESIRED SKILLS\nExperience with applied machine learning techniques such as classification, clustering, dimensionality reduction, anomaly detection, ranking, similarity analysis, natural language processing, or time-series analysis.\n\nExperience working with graph-based data and analytics, including knowledge graphs, network analysis, graph databases, or techniques foridentifyingrelationships and communities across interconnected data.\n\nExperience working with geospatial, temporal, or other specialized data types and incorporating spatial or time-dependent relationships into analytic workflows.\n\nFamiliarity 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.\n\nExperience developing or integrating APIs and lightweight Python services, such asFastAPI, to expose analytic methods or data science capabilities to downstream applications and users.\n\nFamiliarity with deploying applications and analytic capabilities inDockerizedenvironments andutilizingCI/CD pipelines to support repeatable, secure, and maintainable software delivery.\n\nFamiliarity with Git best practices, including branching strategies, pull requests, code reviews, merge conflict resolution, andmaintainingclean, well-documented repositories.\n\nExperience integrating React-based frontends with RESTful APIs and Python-based backend services.\n\nFamiliarity withlarge-scale data processing technologies such as Apache Iceberg, Spark, object storage platforms, or similar modern data architectures.\n\nExperience working in multidisciplinary environments where data science capabilities must be transitioned from exploratory research or prototyping into reliable, maintainable operational tools.\n\nWHAT YOU’LL NEED TO SUCCEED\nClearance:CurrentTS/SCIClearancewithcurrent or willingness to obtain CI polygraph\n\nExperience:10+years of related experience\n\nEducation:Bachelor's degree in Computer Science, Statistics, Engineering, or a related field (or equivalent experience). Advanced degrees are a plus.\n\nRole requirements:This position isonsite in Crystal City, VAand requires travel for 60-90 days to CONUS sites each year.\n\nDue to US Government Contract Requirements, only US Citizens are eligible for this role\n\nGDIT IS YOUR PLACE\nAt GDIT, the mission is our purpose, and our people are at the center of everything we do.\nGrowth: AI-powered career tool thatidentifiescareer steps and learning opportunities\n\nSupport: An internal mobility team focused on helping you achieve your career goals\n\nRewards: Comprehensive benefits and wellness packages, 401K with company match,competitivepayand paid time off\n\nCommunity: Award-winning culture of innovation and a military-friendly workplace\n\nOWN YOUR OPPORTUNITY\nExplore a career at GDIT andyou’llfind endless opportunities to grow alongside colleagues who share your passion for the mission and delivering results.#iexjobs\nEqual Opportunity Employer / Individuals with Disabilities / Protected Veterans\nThe likely salary range for this position is $187,000 - $253,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.Scheduled Weekly Hours:\n40Travel Required:\nNoneTelecommuting Options:\nOnsiteWork Location:\nUSA VA Crystal CityAdditional Work Locations:\nTotal Rewards at GDIT:\nOur benefits package for all US-based employees includes a variety of medical plan options, some with Health Savings Accounts, dental plan options, a vision plan, and a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match. To encourage work/life balance, GDIT offers employees full flex work weeks where possible and a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave. To ensure our employees are able to protect their income, other offerings such as short and long-term disability benefits, life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available. We regularly review our Total Rewards package to ensure our offerings are competitive and reflect what our employees have told us they value most.Our Identity Verification Process:\nAs part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.About Our Work:\nWe are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. 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