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.
