Public Health Surveillance AI Specialist (53265)

Gap Solutions•Atlanta, GA

About The Position

The Public Health Surveillance AI Specialist supports the development of public health analytics by applying biological and public health surveillance expertise to AI-enabled solutions. The role partners with epidemiologists, public health scientists, data scientists, AI engineers, software engineers, and business analysts to evaluate, develop, and operationalize AI capabilities for disease surveillance, situational awareness, and public health decision-making. It translates surveillance questions and biological context into measurable AI use cases, evaluates the quality and reliability of AI-generated outputs, and helps ensure solutions are scientifically sound, explainable, secure, and appropriate for public health use. The specialist evaluates AI and Generative AI applications for surveillance, develops test scenarios and evaluation datasets, assesses outputs for scientific quality and risk, and supports validation of workflows using structured and unstructured public health data. The position also documents findings, communicates with technical and non-technical stakeholders, and stays current on relevant advances in surveillance, epidemiology, biology, Generative AI, and responsible AI. This position is contingent upon contract award.

Requirements

Bachelor’s degree in biology, epidemiology, public health, microbiology, biostatistics, or a related scientific discipline.
At least 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, signals, outbreaks, or other population health indicators.
Experience reviewing scientific or public health information and assessing the accuracy, validity, and appropriate interpretation of analytical results.
Experience working with multidisciplinary teams that include 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 their potential applications and limitations in scientific or public health settings.
Strong understanding of epidemiologic and surveillance concepts, including data quality, case definitions, sensitivity, specificity, timeliness, representativeness, and interpretation of surveillance signals.
Strong communication and documentation skills, including the ability to clearly convey scientific and technical concepts to technical and non-technical audiences.
Must be a U.S. citizen or lawful permanent resident (Green Card holder).

Nice To Haves

Master’s degree or higher in epidemiology, public health, biology, bioinformatics, biostatistics, or a related field.
Experience applying or evaluating Generative AI, large language models, 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 used in public health analytics.
Knowledge of AI evaluation frameworks, benchmark development, prompt evaluation, red teaming, or human-in-the-loop validation.

Responsibilities

Apply biology, epidemiology, and public health surveillance expertise to the design and evaluation of AI-enabled surveillance solutions.
Collaborate with cross-functional teams to understand public health needs and translate them into AI use cases, evaluation criteria, and analytical workflows.
Evaluate AI and Generative AI tools for surveillance applications, including event detection, case and outbreak summarization, information extraction, classification, trend identification, and evidence synthesis.
Develop representative test scenarios and evaluation datasets using surveillance data, scientific literature, reports, and other relevant public health information.
Assess AI outputs for scientific accuracy, completeness, relevance, consistency, hallucinations, bias, explainability, and appropriate use of supporting evidence.
Support validation of AI-assisted workflows using structured and unstructured public health data, including laboratory, case, syndromic, genomic, environmental, and other surveillance information as applicable.
Work with AI and data engineering teams to improve prompts, retrieval-augmented generation (RAG), agents, knowledge sources, and evaluation methods based on public health domain findings.
Document evaluation results, limitations, risks, and recommendations, and communicate findings clearly to technical and non-technical stakeholders.
Stay current with advancements in public health surveillance, epidemiology, biology, Generative AI, and responsible AI, and assess their applicability to public health programs.
Engage in knowledge sharing and contribute to a collaborative team environment.