AI/ML Data Scientist

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $107.9k - $195.1k / year

⏰ Full Time

🟠 Senior

🔴 Lead

📊 Data Scientist

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Leidos

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1969

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

Leidos is a leading systems integrator in science, technology, and engineering, providing solutions that transform and enable the missions of its customers. The company operates across various markets, including aviation, defense, energy, government, healthcare, intelligence, science, and space. Leidos is involved in AI, digital modernization, cyber operations, and integrated and mission software systems. With a commitment to diversity, equity, inclusion, and sustainability, Leidos also engages in charitable efforts and community enrichment programs. Additionally, it contributes to developing solutions for counter-unmanned aerial systems and electric vehicle infrastructure for military applications.

📋 Description

• 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 and assess data readiness • 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 • Support AI-enabled MVPs, technical demonstrations, automation pilots, and rapid experimentation • Evaluate commercial, Government, and open-source AI/ML models and tools • Conduct exploratory data analysis, statistical modeling, data mining, and advanced analytics • Identify trends, patterns, anomalies, and operational insights for Coast Guard leadership • 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 • Establish repeatable data science methodologies, analytical standards, and best practices • Conduct data readiness assessments covering availability, ownership, quality, completeness, lineage, authoritative sources, and accessibility • Clean, normalize, transform, and prepare structured and unstructured datasets • Diagnose data-quality issues and recommend corrective actions • Support data pipelines, ETL processes, reusable analytical data models, and integration across Coast Guard systems and enterprise data platforms • Transition successful prototypes into scalable production environments • Support automation opportunity assessments, feasibility analyses, pilot evaluations, and business process reengineering • Integrate AI/ML capabilities into workflow automation, ServiceNow, Power Platform, Appian, Salesforce, and other approved enterprise platforms • Support intelligent document processing, classification, entity extraction, summarization, forms digitization, workflow generation, and AI-assisted process automation • Support mission modeling and simulation, scenario planning, operational experimentation, forecasting, and trade-space analysis • Translate analytical outputs into actionable recommendations for Coast Guard leadership • Address cybersecurity, data sensitivity, privacy, access control, authorization, responsible AI, explainability, monitoring, and model documentation requirements • Support ATO/cATO-related reviews and technical security documentation • Participate in Agile planning, backlog refinement, sprint reviews, demonstrations, release activities, and user feedback sessions • Translate use cases into AI/ML solutions with product owners, developers, analysts, architects, engineers, and mission stakeholders • Support stakeholder briefings and measure user adoption, operational impact, workload reduction, and “minutes back to mission”

🎯 Requirements

• 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 Master’s 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, and 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: experience supporting DHS, USCG, DoD, or other Federal agencies; agentic AI, embeddings, vector databases, AI orchestration frameworks; ServiceNow, Power Platform, Appian, Salesforce, or similar enterprise workflow environments; data governance, metadata management, lineage, and authoritative data-source identification; NIST AI RMF, NIST 800-53, Zero Trust, ATO/cATO, and Federal AI governance; CUI, PII/SPII, or other sensitive Government data; 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