Health AI ML Data Scientist

Guidehouse•Tysons, VA

2d•$85,000 - $141,000•Remote

About The Position

Design, develop, implement, and maintain AI and machine learning solutions that improve healthcare, public health, biomedical research, and operational decision-making. Identify opportunities to improve organizational efficiency by applying AI, automation, and advanced analytics to streamline business processes, reduce manual effort, and enhance workforce productivity. Design and implement AI-enabled workflow solutions for document processing, knowledge management, literature reviews, information retrieval, case management, reporting, decision support, and other operational functions. Develop Generative AI (GenAI) solutions using large language models (LLMs), retrieval augmented generation (RAG), semantic search, vector databases, AI agents, and prompt engineering techniques. Develop predictive, classification, clustering, natural language processing (NLP), computer vision, and other machine learning models using structured, semi-structured, and unstructured data. Develop scalable data science pipelines using Python, SQL, Spark, and cloud-native analytics platforms. Evaluate, validate, and monitor AI models for performance, explainability, fairness, bias, reproducibility, and operational effectiveness. Collaborate with epidemiologists, researchers, engineers, program staff, and business stakeholders to identify AI use cases, prioritize opportunities, and translate business needs into production-ready AI solutions. Develop dashboards, visualizations, technical documentation, presentations, and demonstrations that communicate AI capabilities and analytical findings to technical and non-technical audiences. Support deployment, monitoring, and continuous improvement of AI and machine learning solutions using modern MLOps and DevSecOps practices. Apply responsible AI principles, governance, privacy, security, and human-in-the-loop review throughout the AI lifecycle. Mentor junior team members and provide technical leadership, solution design, and code reviews as appropriate.

Requirements

Bachelor's degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, Public Health, or related field (or equivalent professional experience).
Experience developing AI and machine learning solutions using Python and common data science libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, or similar frameworks.
Experience applying AI to automate workflows, improve business processes, or enhance operational efficiency.
Experience developing Generative AI applications using large language models (LLMs), prompt engineering, retrieval augmented generation (RAG), or similar techniques.
Experience developing statistical models, predictive analytics, classification, clustering, regression, or natural language processing (NLP) solutions.
Strong SQL skills and experience working with relational databases and cloud-based data platforms.
Experience preparing, integrating, transforming, and analyzing structured and unstructured data to support AI, machine learning, and advanced analytics solutions.
Experience deploying AI or machine learning solutions into production environments.
Understanding of responsible AI principles, model evaluation, explainability, governance, and human oversight.
Experience working in Agile software development environments and fast-paced, matrixed organizations
Strong analytical, problem-solving, written, and verbal communication skills.
Ability to work independently and collaboratively in multidisciplinary technical teams.
U.S. Citizenship required.
Ability to obtain and maintain a Public Trust or higher federal security clearance, as required.

Nice To Haves

Experience implementing AI copilots, intelligent assistants, AI agents, workflow automation, or enterprise knowledge management solutions.
Experience with Azure OpenAI, OpenAI, Anthropic Claude, Amazon Bedrock, Google Vertex AI, Microsoft Copilot Studio, LangChain, LangGraph, LlamaIndex, Hugging Face, or similar AI frameworks.
Experience with Databricks, Snowflake, Microsoft Fabric, Azure AI Foundry, Palantir Foundry, or other enterprise AI and analytics platforms.
Experience with MLOps platforms such as MLflow, Azure ML, SageMaker, Kubeflow, or similar technologies.
Experience supporting healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives.
Experience working with healthcare interoperability standards such as FHIR, HL7, LOINC, SNOMED CT, ICD-10, or OMOP.
Experience implementing AI governance frameworks, model risk management, human-in-the-loop validation, and AI assurance processes.
Experience supporting federal agencies such as CDC, HHS, NIH, CMS, FDA, or VA.
Experience in consulting environments supporting multidisciplinary technical teams.
Relevant AI, cloud, or data platform certifications.

Responsibilities

Design, develop, implement, and maintain AI and machine learning solutions that improve healthcare, public health, biomedical research, and operational decision-making.
Identify opportunities to improve organizational efficiency by applying AI, automation, and advanced analytics to streamline business processes, reduce manual effort, and enhance workforce productivity.
Design and implement AI-enabled workflow solutions for document processing, knowledge management, literature reviews, information retrieval, case management, reporting, decision support, and other operational functions.
Develop Generative AI (GenAI) solutions using large language models (LLMs), retrieval augmented generation (RAG), semantic search, vector databases, AI agents, and prompt engineering techniques.
Develop predictive, classification, clustering, natural language processing (NLP), computer vision, and other machine learning models using structured, semi-structured, and unstructured data.
Develop scalable data science pipelines using Python, SQL, Spark, and cloud-native analytics platforms.
Evaluate, validate, and monitor AI models for performance, explainability, fairness, bias, reproducibility, and operational effectiveness.
Collaborate with epidemiologists, researchers, engineers, program staff, and business stakeholders to identify AI use cases, prioritize opportunities, and translate business needs into production-ready AI solutions.
Develop dashboards, visualizations, technical documentation, presentations, and demonstrations that communicate AI capabilities and analytical findings to technical and non-technical audiences.
Support deployment, monitoring, and continuous improvement of AI and machine learning solutions using modern MLOps and DevSecOps practices.
Apply responsible AI principles, governance, privacy, security, and human-in-the-loop review throughout the AI lifecycle.
Mentor junior team members and provide technical leadership, solution design, and code reviews as appropriate.

Benefits

Medical, Rx, Dental & Vision Insurance
Personal and Family Sick Time & Company Paid Holidays
Parental Leave
401(k) Retirement Plan
Group Term Life and Travel Assistance
Voluntary Life and AD&D Insurance
Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
Transit and Parking Commuter Benefits
Short-Term & Long-Term Disability
Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
Employee Referral Program
Corporate Sponsored Events & Community Outreach
Care.com annual membership
Employee Assistance Program
Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
Position may be eligible for a discretionary variable incentive bonus