Statistician and Data Scientist

Tanaq Management Services•Atlanta, GA

14h•Hybrid

About The Position

Tanaq Management Services (TMS) is seeking a Statistician and Data Scientist to provide expert statistical and data science support to the Global Health Center (GHC), Division of Global HIV and TB (DGHT), at the Centers for Disease Control and Prevention (CDC). This is a hybrid remote position based in the United States. Some ad hoc domestic travel may be required. The role applies advanced statistical, survey, network, and data science methods to HIV- and TB-related public health data. Work includes developing sampling and estimation approaches; estimating population size and HIV incidence; producing methods and tools for respondent-driven sampling and complex network data; and modeling resource allocation. The position also develops open-source software, applications, dashboards, and reports; manages and documents datasets; supports country data submission and quality review; and applies machine learning, simulation, forecasting, clustering, and Bayesian estimation to PEPFAR/AFGHS data. The Statistician and Data Scientist prepares and reviews technical reports, abstracts, protocols, and peer-reviewed manuscripts; trains CDC staff and country teams; and leads seminars and other activities to support practical, responsible AI use. The role requires the ability to telework from within the United States, work core hours, travel domestically on an ad hoc basis, and obtain government clearance. Applicants must be legally authorized to work in the United States without current or future employer sponsorship.

Requirements

At least 10 years of experience as a statistician or data scientist, including public health surveillance, survey sampling, and estimation methods for HIV, TB, or other health programs.
Expert knowledge of complex survey analysis, RDS, population size estimation, longitudinal and multilevel analysis, and time-to-event analysis.
Expert programming skills in R and Python.
Experience with SQL, Stata, SAS, or SPSS, and use of Git/GitHub, Quarto, and Shiny.
Record of peer-reviewed publications in statistics, epidemiology, or machine learning.
Ability to explain complex methods to technical and non-technical audiences and train staff.
Ability to telework from within the United States, work core hours, and travel domestically on an ad hoc basis.
Ability to obtain government clearance.
Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Doctoral degree (Ph.D.) in Statistics, Biostatistics, or a closely related quantitative field. A master's degree with substantial additional experience may be considered.
Prolonged periods of sitting at a desk and working on a computer; may occasionally need to lift up to 25 pounds.

Nice To Haves

Experience with PEPFAR, CDC, or other global health surveillance programs, including methods used in routine practice by country programs.
Authorship of open-source statistical packages or applications used by public health agencies.
Experience with mathematical modeling (SI, SIR, SEIR, agent-based) and machine learning methods.
Experience building and teaching AI-assisted workflows, including approved generative AI tools, with attention to responsible use.
Experience with CDC clearance processes for publications and software releases.
Knowledge of R (RDS, RDS2, pnspop, Rcpp, Shiny, Quarto), Python, C++, Java, Kotlin/Jetpack Compose, Node.js, SQL, Stan, LaTeX, Git, Docker, Spark, HBase, Kafka, SAS, SPSS, and Stata.
Knowledge of AI tools including Claude, ChatGPT, Codex, and MCP.

Responsibilities

Perform advanced analysis of HIV- and TB-related data, including complex sample survey, longitudinal, multilevel, RDS, population size estimation, and time-to-event analyses using R, Stata, RDS-Analyst, and Python.
Design sampling plans and estimation approaches for surveillance and surveys, including continuous facility-based alternatives to periodic behavioral surveys.
Lead analysis workshops with country teams.
Develop and apply methods for population size estimation, including Bayesian synthesis, latent class, and network-based approaches.
Estimate HIV incidence from recency assay and testing history data, and evaluate accuracy using simulation.
Produce estimators and tools for complex network data and RDS, including improved point and variance estimators and field-friendly tools for network designs.
Build models to allocate testing resources by predicted yield and support program planning and forecasting across countries.
Develop open-source R packages, Shiny and web applications, dashboards, and reports such as Quarto materials so CDC staff and countries can use advanced methods.
Maintain hosted public health web applications.
Manage and combine datasets; review data quality; and document variables, data dictionaries, and metadata with version control in GitHub.
Support countries with routine data submission and data quality review.
Apply machine learning, simulation, forecasting, clustering, and Bayesian estimation to PEPFAR/AFGHS data using R, Python, and related tools.
Prepare technical reports, abstracts, protocols, and peer-reviewed manuscripts; review and improve scientific documents; and work with CDC principals on papers and clearance.
Design and deliver short courses on statistical topics and software to CDC staff, including international training delivered virtually or through domestic sessions.
Lead seminars, office hours, and a user group on practical AI for CDC staff.
Write guidance for approved AI tools and build AI connectors and skills for diagnostics and report building, ensuring human review of outputs.