{"id":730303,"url":"https://alion.io/job/leidos-aiml-data-scientist","title":"AI/ML Data Scientist","company":{"id":7791,"name":"Leidos","domain":"leidos.com","url":"https://alion.io/company/leidos","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":90,"open_postings":115,"ghost_share":0,"stale_share":0.609,"repost_share":0.017,"time_to_fill_p50_days":21,"computed_at":"2026-10-01T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":107900,"max":195050,"currency":"USD","period":"year","gross":null,"usd_annual":195050},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Hadoop","optional":false},{"name":"Hugging Face","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"ServiceNow","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"AI Agents","optional":true},{"name":"Embeddings","optional":true},{"name":"NIST 800-53","optional":true},{"name":"NIST AI RMF","optional":true},{"name":"Zero Trust","optional":true}],"status":"closed","first_seen_at":"2026-09-10T00:00:00Z","employer_posted_date":"2026-09-10","last_verified_at":"2026-09-29T19:31:12Z","board_verified":false,"closed_at":"2026-09-29T19:31:12Z","days_open":19,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":19},"description":"The AI/ML Data Scientist will work closely with mission stakeholders, business process analysts, data analysts, AI/ML engineers, automation engineers, enterprise architects, data engineers, cybersecurity personnel, and program leadership to identify high-value use cases, assess data readiness, develop predictive and prescriptive analytics solutions, support rapid MVP pilots, and transition successful solutions toward enterprise-scale implementation.\nThe role will support TRT’s “Start Small, Move Fast” approach by rapidly evaluating whether AI is appropriate for a mission problem, developing and testing prototypes, measuring performance and mission value, and helping mature successful solutions for operational use.\nPrimary Responsibilities\nAI/ML Solution Development\nDesign, develop, test, and evaluate AI/ML solutions supporting Coast Guard mission and business operations.\n\nBuild predictive, prescriptive, classification, anomaly detection, NLP, generative AI, and other advanced analytical solutions.\n\nDevelop, train, tune, and validate machine learning models that improve operational decision-making, workforce productivity, and mission effectiveness.\n\nSupport AI-enabled MVPs, technical demonstrations, automation pilots, and rapid experimentation efforts.\n\nEvaluate commercial, Government, and open-source AI/ML models and tools for mission applicability.\n\nData Science and Analytics\nConduct exploratory data analysis, statistical modeling, data mining, and advanced analytics using structured and unstructured data.\n\nIdentify trends, patterns, anomalies, and operational insights to support Coast Guard leadership decisions.\n\nEstablish model baselines, performance metrics, acceptance criteria, and test methodologies.\n\nAssess model accuracy, reliability, false-positive/false-negative rates, bias, limitations, and operational suitability.\n\nDevelop dashboards, visualizations, analytical products, and performance measures supporting enterprise transformation initiatives.\n\nEstablish repeatable data science methodologies, analytical standards, and best practices.\n\nData Readiness, Engineering, and Integration\nConduct data readiness assessments covering availability, ownership, quality, completeness, lineage, authoritative sources, and accessibility.\n\nClean, normalize, transform, and prepare structured and unstructured datasets for AI/ML analysis.\n\nDiagnose data-quality issues and recommend corrective actions.\n\nSupport development and optimization of data pipelines, ETL processes, and reusable analytical data models.\n\nSupport integration of data from multiple Coast Guard systems, repositories, and enterprise data platforms.\n\nCollaborate with data engineers and AI/ML engineers to transition successful prototypes into scalable production environments.\n\nAutomation and Digital Transformation\nSupport automation opportunity assessments, feasibility analyses, and pilot evaluations.\n\nCollaborate with automation engineers to integrate AI/ML capabilities into workflow automation, ServiceNow, Power Platform, Appian, Salesforce, and other approved enterprise platforms.\n\nParticipate in business process reengineering efforts and identify opportunities to reduce manual effort through AI, automation, and advanced analytics.\n\nSupport intelligent document processing, classification, entity extraction, summarization, forms digitization, workflow generation, and AI-assisted process automation.\n\nMission Modeling and Decision Support\nSupport mission modeling and simulation initiatives that evaluate mission execution, staffing models, operational impacts, and technology alternatives.\n\nDevelop analytical models supporting scenario planning, operational experimentation, forecasting, and trade-space analysis.\n\nTranslate analytical outputs into actionable recommendations for Coast Guard leadership.\n\nSupport data-driven decision advantage by connecting operational requirements, mission outcomes, and analytical results.\n\nAI Governance, Security, and Responsible Use\nWork with ISSO and ISSE personnel to address cybersecurity, data sensitivity, privacy, access control, and authorization requirements.\n\nSupport responsible AI practices, including human-in-the-loop decision processes, explainability, monitoring, and documentation of model limitations.\n\nDocument assumptions, methodologies, model risks, test results, and lessons learned.\n\nSupport ATO/cATO-related reviews and technical security documentation as required.\n\nAgile Development and Collaboration\nParticipate in Agile planning, backlog refinement, sprint reviews, demonstrations, release activities, and user feedback sessions.\n\nWork with product owners, developers, analysts, architects, engineers, and mission stakeholders to translate use cases into AI/ML solutions.\n\nSupport technical demonstrations and stakeholder briefings.\n\nHelp measure user adoption, operational impact, workload reduction, and “minutes back to mission.”\n\nRequired Qualifications\nBachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, Artificial Intelligence, Engineering, Information Systems, Operations Research, or related technical field and 8 - 12 years of prior relevant experience or Masters with 6 - 10 years of prior relevant experience\n\n8+ years of experience in data science, machine learning, artificial intelligence, advanced analytics, or related disciplines.\n\nExperience developing, evaluating, and deploying machine learning models.\n\nStrong proficiency with:\nPython\n\nSQL\n\nScikit-Learn\n\nTensorFlow and/or PyTorch\n\nHugging Face or similar AI/ML frameworks\n\nExperience with predictive analytics, statistical analysis, data mining, and model evaluation.\n\nExperience working with large, complex, structured and unstructured datasets.\n\nExperience developing Generative AI and Large Language Model solutions.\n\nExperience with Retrieval Augmented Generation architectures.\n\nExperience with cloud and data platforms such as AWS, Azure, GovCloud, Databricks, Apache Spark, Hadoop, Kafka, Airflow, or AWS Glue.\n\nExperience integrating AI/ML capabilities with enterprise applications, workflow platforms, APIs, or data services.\n\nStrong written and verbal communication skills with the ability to brief technical and non-technical stakeholders.\n\nU.S. Citizenship required.\n\nAbility to obtain and maintain a DHS Public Trust.\n\nPreferred Qualifications\nExperience supporting DHS, USCG, DoD, or other Federal agencies.\n\nExperience with agentic AI, embeddings, vector databases, or AI orchestration frameworks.\n\nExperience with ServiceNow, Power Platform, Appian, Salesforce, or similar enterprise workflow environments.\n\nExperience with data governance, metadata management, lineage, and authoritative data-source identification.\n\nFamiliarity with NIST AI RMF, NIST 800-53, Zero Trust, ATO/cATO, and Federal AI governance requirements.\n\nExperience supporting CUI, PII/SPII, or other sensitive Government data.\n\nExperience supporting Agile, rapid prototyping, or 12-week MVP delivery environments.\n\nDesired Certifications\nAWS Certified Machine Learning Engineer\n\nAWS Certified Data Engineer or Solutions Architect\n\nMicrosoft Azure AI Engineer\n\nDatabricks Data Engineer / Machine Learning certification\n\nRelevant AI/ML, cloud, or data science certification\n\nIf you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares.\nOriginal Posting:\nSeptember 10, 2026For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.\nPay Range:\nPay Range $107,900.00 - $195,050.00The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.","description_format":"text","description_chars":8326,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":true,"languages":[]},"benefits":["Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["National Security","C4ISR","Defense Manufacturing","Engineering Services"],"lifecycle":[{"event":"open","at":"2026-09-11T09:36:24Z"},{"event":"close","at":"2026-09-29T19:31:12Z"}],"liveness":null,"pay":{"stated_usd_annual":195050,"is_top_pay":false},"html_url":"https://alion.io/job/leidos-aiml-data-scientist","json_url":"https://alion.io/job/leidos-aiml-data-scientist.json","meta":{"generated_at":"2026-10-01T10:59:42Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2262,"day_limit":5000,"remaining_today":2738,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}