Health AI ML Data Scientist

About the role

Guidehouse is seeking a skilled Data Scientist to join our team. This role involves designing, developing, and maintaining AI and machine learning solutions to improve healthcare, public health, biomedical research, and operational decision-making.

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.

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.

Qualifications

• 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.

Skills

• Python
• SQL
• Spark
• Cloud-native analytics platforms
• Generative AI (GenAI)
• Large language models (LLMs)
• RAG
• Retrieval augmented generation (RAG)
• Retrieval augmented generation (RAG)
• Vector databases
• AI agents
• Prompt engineering
• Structured, semi-structured, and unstructured data
• Scikit-learn
• TensorFlow
• PyTorch
• MLflow
• Azure ML
• SageMaker
• Kubeflow
• Microsoft Copilot Studio
• LangChain
• LangGraph
• LlamaIndex
• Hugging Face
• Palantir Foundry
• FHIR
• HL7
• LOINC
• SNOMED CT
• IDC-10
• OMOP
• Model risk management
• Human-in-the-loop validation
• AI assurance processes
• Healthcare interoperability standards
• Consulting environments
• Enterprise knowledge management solutions
• AI governance frameworks
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare interoperability standards
• Consulting environments
• Enterprise knowledge management solutions
• AI governance frameworks
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives
• Healthcare, public health, biomedical research, life sciences, surveillance, or real-world evidence (RWE) initiatives