Staff Scientist

The scientific environment at NIEHS provides exciting opportunities for collaborative and interdisciplinary statisticians. Research at NIEHS increasingly integrates complex exposure science with high-dimensional data from epidemiological studies, genomics, epigenomics, metabolomics, phenomics, electronic health record-derived outcomes, geospatial data, and other large-scale molecular and clinical resources. These settings require rigorous and interpretable analytic strategies that move beyond association towards understanding which signals are the most meaningful. Impactful research integrated across multiple disciplines requires approaches that can be adapted to different settings and domains, and is reproducible, well documented, and actionable.

These positions will involve both the development and application of new statistical and computational methods for the analysis of environmental health related data at the intersection of core biostatistics, statistical genetics and genomics, and precision environmental health, including causal and computational approaches to exposomics.

The successful candidates will collaborate extensively with researchers in the BCBB and scientists from diverse disciplines to advance statistical science on the environment and human health. The successful candidates will work with investigators within BCBB, and with investigators across the Institute, including epidemiologists, clinical researchers, and laboratory scientists, on motivating data applications at their discretion.

Preferred Candidates will have expertise which builds on or complements the Branch's existing strengths in core biostatistics, statistical genetics, computational biology, and collaborative environmental health science, and who expand our portfolio in an interdisciplinary direction.

Preferred Candidates will have experience in one or more of the following areas: causal inference for complex environmental exposures and mixtures, longitudinal and time-varying exposures, mediation and effect heterogeneity, exposomics and metabolomics and proteomics integration, Bayesian or hierarchical modeling, kernel and penalized regression methods, latent variable or clustering approaches, multilevel and longitudinal modeling, geospatial environmental or social exposure modeling, and integrative approaches for multimodal data.

Appointments will be at the rank of Staff Scientist 1 in the Biostatistics and Computational Biology Branch (BCBB) of the Division of Intramural Research (DIR).

Minimum qualifications include:

• A doctoral degree, preferably in statistics, biostatistics, data science, human genetics, genetic epidemiology, environmental health, epidemiology, computational biology, or a closely related quantitative field.
• At least two years of postdoctoral research or relevant work experience.
• Candidates at different career stages are welcome to apply, and accomplishments will be evaluated in the context of career stage and opportunity.
• Strong candidates will demonstrate a record of peer-reviewed scholarship, increasing scientific independence and responsibility, and meaningful contributions to multidisciplinary research.
• The ability to translate biomedical or public health questions into rigorous and feasible quantitative studies is particularly important.
• Excellent oral and written communication skills and the ability to work effectively with investigators from varied scientific backgrounds are required.

For the statistically focused position, candidates should demonstrate strong training and experience in statistical reasoning, methods, study design, and inference. Relevant areas may include high-dimensional data analysis, statistical genetics or genomics, multimodal data integration, longitudinal or correlated data, causal inference, measurement error, missing data, batch effects, selection bias, or harmonization across studies. Experience developing, evaluating, or adapting statistical methods to address substantive biomedical or environmental health questions is particularly desirable.

For the position focused on large-scale biomedical data resources, particular consideration will be given to candidates with experience leading or co-leading research using All of Us or another major population-based cohort, biobank, or electronic health record resource. Relevant experience may include integrating genomic, omic, clinical, survey, behavioral, wearable, or geospatial data; working in secure or cloud-based research environments; developing scalable and reproducible analytic workflows; and building productive collaborations between quantitative and domain scientists. Direct experience with All of Us is welcome but is not required.

Appointees may be a US Citizen, Legal Permanent Resident or non-US citizen eligible for a valid work authorization. This position is subject to a background investigation. Please read the following guidance on Selective Service requirements at https://help.usajobs.gov/working-in-government/fair-and-transparent/selective-service.