Data Scientist/AI Lead

Program Management Solutions LLC • Rockville, MD

6d • Onsite

About The Position

Program Management Solutions is looking for a Data Scientist/AI Lead to join our growing team. This is an on-site position that's located in Rockville, MD.

Requirements

• Bachelor’s degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Bioinformatics, Biomedical Informatics, Computational Biology, Engineering, or related field.
• Minimum 10+ years of experience supporting data science, artificial intelligence, machine learning, informatics, analytics, data engineering, or scientific computing initiatives.
• Minimum 5+ years of experience leading technical teams supporting data-intensive, AIenabled, or scientific computing environments.
• Experience designing and implementing data architectures, ingestion pipelines, metadata management, and data governance frameworks.
• Experience supporting FAIR data principles, data harmonization, interoperability, and scientific data lifecycle management.
• Experience developing, integrating, or supporting AI/ML-enabled analytics, predictive modeling, automation, or advanced computational workflows.
• Experience designing, deploying, or supporting Generative AI (GenAI) solutions, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent assistants, AI agents, prompt engineering, and enterprise knowledge management solutions.
• Experience supporting responsible AI practices, including model documentation, transparency, explainability, performance monitoring, and bias identification or mitigation.
• Experience supporting cloud, high-performance computing, GPU-enabled, or large-scale analytics platforms.
• Experience implementing Machine Learning Operations (MLOps) practices including model lifecycle management, model registries, feature engineering pipelines, automated retraining, version control, deployment automation, and continuous model monitoring.
• Experience supporting vector databases, semantic search, knowledge graphs, embeddings, enterprise search, and AI-enabled knowledge management platforms.
• Experience briefing technical and executive stakeholders.

Nice To Haves

• Master’s degree or higher in a related field.
• Experience supporting NIH, HHS, or biomedical research organizations.
• Experience with bioinformatics, cheminformatics, knowledge graphs, ontology management, and translational science data environments.
• Experience supporting AI-enabled analytics, machine learning operations, advanced scientific computing, high-content imaging, or large-scale biomedical data environments.
• Experience with human-in-the-loop AI workflows, image segmentation, model validation, or AI-enabled scientific platform integration.
• Experience supporting enterprise Generative AI platforms using Azure OpenAI, Amazon Bedrock, Google Vertex AI, OpenAI APIs, Hugging Face, LangChain, Semantic Kernel, or equivalent technologies.
• Experience supporting AI governance, AI security, prompt management, model evaluation, AI red teaming, or enterprise AI adoption initiatives.
• Experience integrating GenAI capabilities into scientific research, biomedical informatics, or decision-support environments.

Responsibilities

• Designing and implementing data architectures, ingestion pipelines, metadata management, and data governance frameworks.
• Supporting FAIR data principles, data harmonization, interoperability, and scientific data lifecycle management.
• Developing, integrating, or supporting AI/ML-enabled analytics, predictive modeling, automation, or advanced computational workflows.
• Designing, deploying, or supporting Generative AI (GenAI) solutions, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent assistants, AI agents, prompt engineering, and enterprise knowledge management solutions.
• Supporting responsible AI practices, including model documentation, transparency, explainability, performance monitoring, and bias identification or mitigation.
• Supporting cloud, high-performance computing, GPU-enabled, or large-scale analytics platforms.
• Implementing Machine Learning Operations (MLOps) practices including model lifecycle management, model registries, feature engineering pipelines, automated retraining, version control, deployment automation, and continuous model monitoring.
• Supporting vector databases, semantic search, knowledge graphs, embeddings, enterprise search, and AI-enabled knowledge management platforms.
• Briefing technical and executive stakeholders.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior