Public Health Surveillance AI Specialist
Location: Remote
Employment Type: Contract
Job Description
The DMAC AI/ML Project Team is seeking a Biology/Public Health Surveillance AI Specialist to support the next generation of public health analytics by applying biological and public health surveillance expertise to AI-enabled solutions.
The specialist will collaborate with epidemiologists, public health scientists, data scientists, AI engineers, software engineers, and business analysts to evaluate, develop, and operationalize AI capabilities that strengthen disease surveillance, situational awareness, and public health decision-making.
The ideal candidate will have a strong background in biology, epidemiology, or public health surveillance, along with an understanding of AI, Generative AI, and large language models (LLMs).
Key Responsibilities
- Apply biology, epidemiology, and public health surveillance expertise to the design and evaluation of AI-enabled surveillance solutions.
- Translate public health requirements into measurable AI use cases, evaluation criteria, and analytical workflows.
- Evaluate AI and Generative AI tools for event detection, outbreak summarization, information extraction, classification, trend identification, and evidence synthesis.
- Develop representative test scenarios and evaluation datasets using surveillance data, scientific literature, and public health reports.
- Assess AI-generated outputs for scientific accuracy, completeness, relevance, consistency, hallucinations, bias, and explainability.
- Support validation of AI-assisted workflows involving structured and unstructured public health data, including laboratory, case, syndromic, genomic, and environmental surveillance data.
- Collaborate with AI and data engineering teams to improve prompts, Retrieval-Augmented Generation (RAG), AI agents, knowledge sources, and evaluation methods.
- Document evaluation results, limitations, risks, and recommendations.
- Communicate findings effectively to technical and non-technical stakeholders.
- Stay current with advancements in public health surveillance, epidemiology, biology, Generative AI, and responsible AI.
Required Qualifications
- 5+ years of experience in biology, epidemiology, public health, biosurveillance, infectious disease surveillance, or a closely related field.
- Experience working with public health surveillance data and interpreting disease trends, outbreak signals, and population health indicators.
- Experience reviewing scientific or public health information and assessing the accuracy and validity of analytical results.
- Experience collaborating with multidisciplinary teams, including epidemiologists, scientists, data analysts, data engineers, software engineers, or AI/ML practitioners.
- Strong understanding of AI, Machine Learning, Generative AI, or Large Language Model capabilities and limitations.
- Strong understanding of epidemiologic and surveillance concepts, including:
- Data quality
- Case definitions
- Sensitivity and specificity
- Timeliness and representativeness
- Interpretation of surveillance signals
- Excellent communication and documentation skills.
- Bachelor's degree in Biology, Epidemiology, Public Health, Microbiology, Biostatistics, or a related scientific discipline.
Preferred Qualifications
- Experience applying or evaluating Generative AI, LLMs, RAG, AI agents, or natural-language analytics in public health, healthcare, or life sciences.
- Experience with public health surveillance platforms, laboratory or genomic data, syndromic surveillance, or emerging infectious disease monitoring.
- Experience working with federal, state, local, tribal, or territorial public health programs.
- Knowledge of Python, R, SQL, statistical analysis, or data visualization tools.
- Knowledge of AI evaluation frameworks, benchmark development, prompt evaluation, red teaming, or human-in-the-loop validation.
- Master's degree or higher in Epidemiology, Public Health, Biology, Bioinformatics, Biostatistics, or a related field.
Work Environment
- Remote Position
- Collaborative, multidisciplinary AI/ML project environment
