Data Scientist

Responsibilities

Designs, configures, develops, tests, and supports informatics and data science solutions for a wide array of technical use cases
Collaborate with cross-functional teams, including data scientists and software engineers, to integrate AI solutions developed across the DoD community into Search Portfolio products when appropriate
Optimize AI models for performance, scalability, and efficiency, leveraging cloud-based resources and distributed computing frameworks, specifically Apache Spark/Databricks; ability to adapt code base to also run using GPU-enabled Kubernetes clusters
Stay updated on and contribute to the latest advancements in AI research, applying new findings to improve Search Portfolio products
Manage the lifecycle of AI/ML components used in Search Portfolio products from research and development to deployment and optimization
Apply analytical methodologies to diagnose data-related challenges, implement solutions, and evaluate performance
Document and present requirements, design alternatives, and findings to team members and clients
Develop strategic, baselined data modeling processes; accurately determine cause-and-effect relationships
Utilize integrated development environments, data integration, data visualization, data mining, and analysis tools
Maintain and guide the development of common libraries and tools used by multiple teams
Aid in formulating a strategy on how to achieve rapid prototyping

Requirements

Bachelor's degree plus 7–10 years of experience, or a Master's degree plus 5 years of experience in a relevant field
Must currently hold an active Top Secret security clearance, or at minimum an active Secret clearance with the ability and willingness to obtain a Top Secret/SCI clearance
Experience with ML fields, including natural language processing, computer vision, and statistical learning theory
Hands-on experience with Natural Language Processing (NLP), Large Language Models (LLMs), text embedding, semantic query, generative AI for text, and Retrieval Augmented Generation (RAG)
Familiarity with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects
Strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks
Experience with version control systems such as Git, enabling effective collaboration and code management
Demonstrated experience in an ML engineer or data scientist role building and deploying ML models
Proficiency writing code in one or more of the following: Python, R, Scala, Java, or C++, with documentation for reproducibility
Experience using Apache Spark/Databricks distributed compute environments for AI/ML workloads
Experience handling petabyte-scale datasets, discovering hidden patterns, utilizing data visualization tools, writing SQL, and working with GPUs to develop models
Experience with cloud-based data persistence products, especially RDS PostgreSQL and PostgreSQL extensions such as pgvector
Strong written and verbal communication skills with the ability to convey technical concepts to business, technical, and lay audiences; experience delivering data-driven presentations
Occasional Travel Required: The candidate may be required to travel occasionally (e.g., 10-15%) to support mission needs, participate in client meetings, or collaborate with cross-functional teams at various DoD/DHA sites

Preferred Qualifications

DoD/DHA Enterprise Experience: Prior experience working within a Department of Defense (DoD) or Defense Health Agency (DHA) enterprise environment, with a strong understanding of military health systems, programs, and organizational structures
Security Certification: Holds a current CompTIA Security+ certification or a higher-level security certification (e.g., CISSP, CASP+, CEH), in compliance with DoD 8570/8140 Information Assurance requirements
Familiarity with DHMSM, MHS GENESIS, or other DoD electronic health record (EHR) systems and associated data ecosystems
Experience working within Agile or DevSecOps environments supporting government programs