AI/ML Data Scientist

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.

The 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.

Primary Responsibilities

AI/ML Solution Development

• Design, develop, test, and evaluate AI/ML solutions supporting Coast Guard mission and business operations.

• Build predictive, prescriptive, classification, anomaly detection, NLP, generative AI, and other advanced analytical solutions.

• Develop, train, tune, and validate machine learning models that improve operational decision-making, workforce productivity, and mission effectiveness.

• Support AI-enabled MVPs, technical demonstrations, automation pilots, and rapid experimentation efforts.

• Evaluate commercial, Government, and open-source AI/ML models and tools for mission applicability.

Data Science and Analytics

• Conduct exploratory data analysis, statistical modeling, data mining, and advanced analytics using structured and unstructured data.

• Identify trends, patterns, anomalies, and operational insights to support Coast Guard leadership decisions.

• Establish model baselines, performance metrics, acceptance criteria, and test methodologies.

• Assess model accuracy, reliability, false-positive/false-negative rates, bias, limitations, and operational suitability.

• Develop dashboards, visualizations, analytical products, and performance measures supporting enterprise transformation initiatives.

• Establish repeatable data science methodologies, analytical standards, and best practices.

Data Readiness, Engineering, and Integration

• Conduct data readiness assessments covering availability, ownership, quality, completeness, lineage, authoritative sources, and accessibility.

• Clean, normalize, transform, and prepare structured and unstructured datasets for AI/ML analysis.

• Diagnose data-quality issues and recommend corrective actions.

• Support development and optimization of data pipelines, ETL processes, and reusable analytical data models.

• Support integration of data from multiple Coast Guard systems, repositories, and enterprise data platforms.

• Collaborate with data engineers and AI/ML engineers to transition successful prototypes into scalable production environments.

Automation and Digital Transformation

• Support automation opportunity assessments, feasibility analyses, and pilot evaluations.

• Collaborate with automation engineers to integrate AI/ML capabilities into workflow automation, ServiceNow, Power Platform, Appian, Salesforce, and other approved enterprise platforms.

• Participate in business process reengineering efforts and identify opportunities to reduce manual effort through AI, automation, and advanced analytics.

• Support intelligent document processing, classification, entity extraction, summarization, forms digitization, workflow generation, and AI-assisted process automation.

Mission Modeling and Decision Support

• Support mission modeling and simulation initiatives that evaluate mission execution, staffing models, operational impacts, and technology alternatives.

• Develop analytical models supporting scenario planning, operational experimentation, forecasting, and trade-space analysis.

• Translate analytical outputs into actionable recommendations for Coast Guard leadership.

• Support data-driven decision advantage by connecting operational requirements, mission outcomes, and analytical results.

AI Governance, Security, and Responsible Use

• Work with ISSO and ISSE personnel to address cybersecurity, data sensitivity, privacy, access control, and authorization requirements.

• Support responsible AI practices, including human-in-the-loop decision processes, explainability, monitoring, and documentation of model limitations.

• Document assumptions, methodologies, model risks, test results, and lessons learned.

• Support ATO/cATO-related reviews and technical security documentation as required.

Agile Development and Collaboration

• Participate in Agile planning, backlog refinement, sprint reviews, demonstrations, release activities, and user feedback sessions.

• Work with product owners, developers, analysts, architects, engineers, and mission stakeholders to translate use cases into AI/ML solutions.

• Support technical demonstrations and stakeholder briefings.

• Help measure user adoption, operational impact, workload reduction, and “minutes back to mission.”

Required Qualifications

• Bachelor’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

• 8+ years of experience in data science, machine learning, artificial intelligence, advanced analytics, or related disciplines.

• Experience developing, evaluating, and deploying machine learning models.

• Strong proficiency with:

  • Python

  • SQL

  • Scikit-Learn

  • TensorFlow and/or PyTorch

  • Hugging Face or similar AI/ML frameworks

• Experience with predictive analytics, statistical analysis, data mining, and model evaluation.

• Experience working with large, complex, structured and unstructured datasets.

• Experience developing Generative AI and Large Language Model solutions.

• Experience with Retrieval Augmented Generation architectures.

• Experience with cloud and data platforms such as AWS, Azure, GovCloud, Databricks, Apache Spark, Hadoop, Kafka, Airflow, or AWS Glue.

• Experience integrating AI/ML capabilities with enterprise applications, workflow platforms, APIs, or data services.

• Strong written and verbal communication skills with the ability to brief technical and non-technical stakeholders.

• U.S. Citizenship required.

• Ability to obtain and maintain a DHS Public Trust.

Preferred Qualifications

• Experience supporting DHS, USCG, DoD, or other Federal agencies.

• Experience with agentic AI, embeddings, vector databases, or AI orchestration frameworks.

• Experience with ServiceNow, Power Platform, Appian, Salesforce, or similar enterprise workflow environments.

• Experience with data governance, metadata management, lineage, and authoritative data-source identification.

• Familiarity with NIST AI RMF, NIST 800-53, Zero Trust, ATO/cATO, and Federal AI governance requirements.

• Experience supporting CUI, PII/SPII, or other sensitive Government data.

• Experience supporting Agile, rapid prototyping, or 12-week MVP delivery environments.

Desired Certifications

• AWS Certified Machine Learning Engineer

• AWS Certified Data Engineer or Solutions Architect

• Microsoft Azure AI Engineer

• Databricks Data Engineer / Machine Learning certification

• Relevant AI/ML, cloud, or data science certification

If 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.

Original Posting:

September 10, 2026

For 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.

Pay Range:

Pay Range $107,900.00 - $195,050.00

The 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.