Artificial Intelligence (AI) Data Scientist

Seize your opportunity to make a personal impact as a Artificial Intelligence (AI) Data Scientist supporting the NIH Administrative Systems Technology Officer (ASTO). GDIT is your place to make meaningful contributions to challenging projects and grow a rewarding career.

At GDIT, people are our differentiator. As an AI Data Scientist you will help ensure today is safe and tomorrow is smarter. Join our innovative team at the forefront of emerging data and AI solutions! We're looking for a charismatic hands-on Senior Advisor to lead groundbreaking initiatives in advanced data through artificial intelligence solutions. Supporting the NIH Administrative Systems Technology Officer, you'll idealize, design, and build state-of-the-art Data & AI-Powered solutions that drive large-scale organizational transformation & outcomes. We take cutting edge technologies / capabilities, idealize, design, and build solutions using them to meet real organization needs, turn these into proof of concepts, prototypes, and then provide the mature capabilities to other teams for operationalization and scaling. This role is strategic, hands on, and collaborates with leaders, stakeholders, and technical teams while shaping the future of technology at NIH.

This role is fully remote!

HOW AN AI DATA SCIENTIST WILL MAKE AN IMPACT:

• Idealize, design and develop advanced prototypes for AI and data solutions
• Innovate in fields like Generative AI, Connected Semantic Data, Knowledge Graphs, Data Fabrics, Chat Bots, Agentic AI, Multimodal AI, LLMs, Predicative Analytics, and Intelligent Search among many others using the latest technologies in scaled solutions
• Transition from prototypes to scalable solutions and operational teams
• Stay updated with emerging technology trends to maintain leadership in the field
• Provide thought leadership and expertise to NIH leadership, stakeholders, and business / technology teams to optimize NIH systems
• Partner with government technology leaders to build the future at NIH
• Lead diverse teams to design, build, and deploy these solutions

WHAT YOU’LL NEED TO SUCCEED:
 

Required Skills & Experience:

• Bachelor's degree and 15+ years of related experience (or equivalent education and/or experience)
• Demonstrated expertise in idealizing, designing, building, and deploying Data Solutions using emergent technology solutions
• 10+ years of experience in Data Fabrics, Knowledge Graphs, or Graphs
• 5+ years of experience with Data Fabrics on Microsoft Azure, AWS, or Databricks is required
• Demonstrated expertise in idealizing, designing, building, and deploying Artificial Intelligence solutions
• 5+ years of experience in: Generative AI, Machine Learning, NLP, Intelligent Search, or LLMs
• 7+ years of proven experience in developing prototypes and transitioning solutions to operational scale
• 7+ years of experience in the cloud using either Microsoft Azure and/or AWS is required
• Experience with data capability, with one of the following: AWS Certified Data Analytics – Specialty, AWS Certified Big Data – Specialty, University Certification or Degree in Data Sciences, Azure Data Scientist, Fabric Analytics Engineer, Azure Data Engineer
• AWS Certified Machine Learning - Specialty and/or Azure AI Engineer Associate are required
• Must be able to obtain a Level 5 Public Trust clearance

Preferred Skills & Experience:

• PhD degree in Computer Science, Artificial Intelligence, Information Sciences, or related field is preferred
• Certification in at least one of the following: AWS Certified Solution Architect – Professional, Azure Solutions Architect Expert, AWS Certified DevOps Professional, Azure DevOps Engineer Expert
• Experience with Microsoft Fabric AND Delta Lakes on Microsoft Azure
• Experience with Knowledge Graphs, Semantic Layers, and/or Ontologies
• Experience with Conversational AI, Agentic AI, Chat Bots, Multimodal AI, and/or Predictive Analytics
• Experience fine-tuning models, model optimization, and/or Graph Analytics
• Proven experience combining Data & AI solutions in both prototypes through to significant scale

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