AI/ML Data Scientist
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Job Description
AI/ML Data Scientist supporting Leidos’ Coast Guard mission analytics, machine learning, and automation initiatives. Developing prototypes, evaluating models, preparing data, and supporting responsible AI delivery.
Responsibilities:
- Support the design, development, testing, and evaluation of AI/ML solutions for Coast Guard mission and business operations.
- Assist with predictive, classification, anomaly detection, NLP, generative AI, and other analytical solutions.
- Support machine learning model training, tuning, validation, and documentation under senior technical guidance.
- Assist with AI-enabled MVPs, technical demonstrations, automation pilots, and rapid experimentation.
- Research and evaluate commercial, Government, and open-source AI/ML models and tools.
- Conduct exploratory data analysis using structured and unstructured datasets.
- Identify trends, patterns, anomalies, and insights to support decision-making.
- Develop model baselines, performance measures, acceptance criteria, test methods, dashboards, visualizations, and analytical summaries.
- Support data readiness assessments, data cleaning, normalization, transformation, data pipelines, ETL processes, reusable analytical datasets, and system integration.
- Support automation assessments and AI/ML integration with enterprise platforms such as ServiceNow, Power Platform, Appian, and Salesforce.
- Participate in business process analysis, intelligent document processing, classification, entity extraction, summarization, forms digitization, and AI-assisted workflow improvements.
- Support mission modeling, simulation, scenario planning, forecasting, operational experimentation, and trade-space analysis.
- Translate analytical outputs into findings and recommendations for technical and non-technical audiences.
- Support data sensitivity, privacy, access control, security, responsible AI, human-in-the-loop review, explainability, model monitoring, and ATO/cATO documentation.
- Participate in Agile planning, backlog refinement, sprint reviews, demonstrations, release activities, and user feedback sessions.
- Collaborate with product owners, developers, analysts, architects, engineers, cybersecurity personnel, and mission stakeholders.
- Prepare technical documentation, demonstration materials, stakeholder briefing inputs, and adoption and operational impact measures.
Requirements:
- Bachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, Artificial Intelligence, Engineering, Information Systems, Operations Research, or a related technical field.
- 0–2 years of experience in data science, machine learning, artificial intelligence, advanced analytics, or a related discipline.
- Foundational experience developing, evaluating, or supporting machine learning models.
- Working knowledge of Python, SQL, Scikit-Learn, TensorFlow and/or PyTorch, and Hugging Face or similar AI/ML frameworks.
- Foundational experience with predictive analytics, statistical analysis, data mining, and model evaluation.
- Experience working with structured and/or unstructured datasets.
- Exposure to Generative AI, Large Language Models, or prompt engineering.
- Exposure to Retrieval Augmented Generation concepts or architectures.
- Familiarity with cloud and data platforms such as AWS, Azure, GovCloud, Databricks, Apache Spark, Hadoop, Kafka, Airflow, or AWS Glue.
- Ability to support integration of AI/ML capabilities with APIs, workflow tools, enterprise applications, or data services.
- Strong written and verbal communication skills.
- U.S. Citizenship required.
- Ability to obtain and maintain a DHS Public Trust.
- Preferred: internship, academic, or early career experience supporting DHS, USCG, DoD, or other Federal agencies.
- Preferred: exposure to agentic AI, embeddings, vector databases, or AI orchestration frameworks.
- Preferred: familiarity with ServiceNow, Power Platform, Appian, Salesforce, or similar enterprise workflow environments.
- Preferred: experience supporting Agile, rapid prototyping, hackathons, academic projects, or MVP-style delivery environments.
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