Job Summary
The National Institute of Environmental Health Sciences (NIEHS) is seeking a dynamic, highly motivated Staff Scientist to support the Predictive Toxicology Branch (PTB) in the Division of Translational Toxicology (DTT), formerly known as the Division of the National Toxicology Program (DNTP). The mission of the DTT is to improve public health through data and knowledge development that are translatable, predictive, and timely. The DTT provides critical data for regulatory and non-regulatory stakeholder decision making to protect human health using epidemiological data, in vivo studies, alternative in vivo model systems, in vitro high-throughput screens and/or computational approaches. To achieve its mission, DTT scientists 1) collaborate with public stakeholders and global partners to identify and address public health issues, 2) generate and communicate trusted information to support decision making on environmental hazards, 3) lead the transformation of toxicology in the development and application of innovative tools and strategies, and 4) educate and train the next generation of translational toxicologists.
DTT's scientific goals are achieved through a distinct, highly cooperative and integrated team science operational model whereby scientific staff across multiple branches assemble into interdisciplinary project teams and utilize centrally managed shared resources, in contrast to the traditional NIH principal investigator-led research group model. The majority of research is carried out through use of external research and development contracts together with onsite intramural research capability.
PTB is a key contributor to DTT's efforts to lead the science of toxicology and environmental public health. Understanding the Social Determinants of Health (SDOH) are a critical element to these efforts. Capabilities within PTB innovate to advance the development, validation, and application of novel analytics, computational tools, dose-response models, high-throughput assays, and systems toxicology models. PTB scientists work collaboratively with internal and external scientific and community partners in problem formulation and application of DTT capabilities for scientific discovery that supports human health. PTB leads the development of innovative software and artificial intelligence (AI) models that take an evidence-based approach to identifying and understanding environmental contributors to public health and disease through research that characterizes chemicals, detects biological activities of significance to humans, probes the genetic and epigenetic bases for differences in disease susceptibility, and integrate these data with findings from traditional toxicology models and human studies.
Roles and Responsibilities: The Staff Scientist will lead a new Group that adds translational capabilities in the Social Sciences and Social Determinants of Health, relevant to NIEHS Strategic Plan areas "Environmental Health Disparities, Environmental Justice, and Health Equity" and "Climate Change Impacts on Human Health". As with other Groups in PTB (https://www.niehs.nih.gov/research/atniehs/labs/ptb), resources to recruit staff and conduct research are shared through sources including Program Management Teams (https://www.niehs.nih.gov/research/atniehs/dtt/strategic-plan), external research and development contracts, and interagency agreements.
As with other Groups in PTB (https://www.niehs.nih.gov/research/atniehs/labs/ptb), resources to recruit staff and conduct research are available through multiple sources. These include intramural funds for the Division and Branch, as well as shared through sources including Program Management Teams (https://www.niehs.nih.gov/research/atniehs/dtt/strategic-plan), external research and development contracts, and interagency agreements. This PTB Staff Scientist will report to the PTB Chief and provide scientific and technical support to the strategic priorities of the DTT.
The successful applicant will be an independent, innovative thinker, possess a strong foundational scientific knowledge in an area of applied social, economic, computational or life sciences. The Staff Scientist will devise and implement strategies for connecting research programs across the Division and Institute with expertise in data science approaches to studying social determinants of health, environmental health disparities, environmental justice, and health equity to develop translational solutions in toxicology and environmental public health. Examples include investigation of the disproportionate impacts of climate change on vulnerable populations, mitigating health risks through community-centered approaches, estimating social and economic impacts related to toxicology and environmental health, and synthesis across multimodal data from internal and external sources. The researcher will collaborate across interdisciplinary teams to develop evidence-based solutions and strategies aimed at fostering health and equity in the face of environmental challenges. This role will require strong skills in data analysis using advanced statistical and/or AI approaches, plus communication of research findings to diverse audiences.
As a NIH Title 42(g) Staff Scientist 1, the employee is generally appointed to a time-limited, renewable position. Staff Scientists do not receive independent resources as a principal investigator, although they will often work independently and have sophisticated skills and knowledge essential to the work of the branch.
Required Qualifications
The ideal candidate for this position will have a research-based professional degree (Ph.D., M.D., Sc.D., Pharm.D., or D.V.M.) in the social, economic, computational, epidemiological, or life sciences. The successful applicant will have demonstrated experience leading diverse scientific research teams and collaborative efforts. The successful candidate must demonstrate the strategic and visionary leadership for identifying and matching new approaches with the needs of the DTT for public health decision-making.
This position is not eligible for fully remote work. The selected candidate shall reside within the Research Triangle Park, NC local commuting area. At the supervisor's discretion, this position may offer work schedule flexibilities to include telework.
Appointee may be a US citizen, Legal Permanent Resident of non-US citizen who is eligible for a valid work authorization.
This position is subject to a background investigation.
Benefits
This is a federal full-time equivalent position, and a comprehensive benefits package is available. Salary will be commensurate with experience, qualifications, and accomplishments.
Equal Employment Opportunity
NIH, NIEHS encourages the application and nomination of traditionally underrepresented groups in the sciences, including women, minorities, and individuals with disabilities. The United States Government does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factor. Equal Employment Opportunity (EEO) for federal employees & job applicants.
Standards of Conduct/Financial Disclosure
If selected, you will be required to complete a Confidential Financial Disclosure Report, OGE Form 450 to determine if a conflict or an appearance of a conflict exists between your financial interest and your prospective position with the agency.
Foreign Education
This position has an education requirement. You are strongly encouraged to submit a copy of your transcripts (or a list of your courses including titles, credit hours completed and grades). Unofficial transcripts will be accepted in the application package. Official transcripts will be required from all selectees prior to receiving an official offer. Learn more about Foreign Education.
Reasonable Accommodation
You can request a reasonable accommodation at any time during the application or hiring process or while on the job. Requests are considered on a case-by-case basis.
