Job Posting: Staff Scientist - (IHAB)

Summary

The National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC is seeking a dynamic, highly motivated Staff Scientist with data science expertise to support the Integrative Health Assessment Branch (IHAB) in the Division of Translational Toxicology, formerly known as the Division of the National Toxicology Program (DNTP). The mission of DTT is to improve public health through data and knowledge development that are translatable, predictive, and timely. 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. Most of the research is carried out through use of external research and development contracts together with onsite intramural research capability.

IHAB is a key contributor to DTT’s efforts to lead the science of toxicology and environmental public health by developing and applying innovative informatics and systematic approaches to identify, categorize, critically assess, and integrate scientific evidence across different types of data to support evidence-based human health assessments of environmental substances. The findings of this research are published in the peer-reviewed literature, NIEHS reports, and National Toxicology Program monographs. Scientific advances are now frequently driven through the analysis of large-scale data gathered from a variety of sources (e.g., genomics, metabolomics, and clinical records). For health assessments and most IHAB research, human health effects conclusions are being enabled by identification and capture of published research through evidence informatics approaches (e.g., artificial intelligence [AI], machine learning [ML], and natural language processing [NLP], and use of large language models [LLMs]) or analysis of the growing collection of scientific knowledge stored in computerized knowledge bases and embedded in computational models. This accumulating wealth of data and knowledge are enabling novel algorithms and machine-driven approaches to extend biomedical knowledge and new analytical, modeling, and visualization approaches to promote the understanding of complex biological systems.

Duties

NIEHS is seeking a staff scientist with data science expertise for IHAB, in DTT, at NIEHS. As a data scientist expert with experience in biomedical research and/or systematic 2 review this staff scientist will provide leadership and technical backing to advance the application of informatic approaches in support of DTT and NIEHS human health assessments and other activities. This staff scientist will provide leadership to meet organizational informatics goals especially to advance the use of evidence informatics approaches, data science methods, tools, and workflow to streamline the identification, critical assessment, and use of public health data in health assessments and evidence-based decision making. Using systematic review methods, these evaluations integrate the evidence from published research on environmental exposures and associated health effects in human and animal studies, along with human exposure and mechanisms data, reflecting the growing complexity of environmental science.

Key responsibilities include and are not limited to:

• Provide computational and informatics methodological expertise for IHAB and DTT activities, such as systematic reviews, systematic evidence maps, scoping projects, and environmental epidemiological and toxicological studies.
• Identify, develop, implement, and improve evidence informatics approaches (e.g., artificial intelligence [AI], machine learning [ML], and natural language processing [NLP], and use of large language models [LLMs]) in systematic review methods, workflow, and tools.
• Conduct analysis and research related to improving data and information processing and workflows related to human health effects assessments.
• Advise and inform DTT and NIEHS researchers and staff on the use of data science methods and tools, acting as an advocate for data science within the institution.
• Work closely with other data scientists, computer scientists, and IT technologists at NIEHS and NIH to build a robust and modern data science infrastructure.
• Initiate research and projects with both health scientists and other data scientists, informaticists, and computer scientists in advancing novel data science methods and resources.
• Serve as technical expert or project lead for assessment teams in conducting evaluations (e.g., systematic reviews, systematic evidence maps) that inform policy decisions and public health practices derived from existing literature and DTT or NTP research.
• Function as a data science expert and liaison between the researchers, technical staff, and other data scientists to provide advice and guidance on the most effective methods for identifying, collecting, critically analyzing, and interpreting data.
• Represent DTT and NIEHS at NTP or NIEHS peer-review meetings, and national and international scientific workshops and meetings.

Who Can Apply

Qualifications

Required Qualifications

Candidates must possess a Ph.D., M.D., Pharm D., or equivalent doctoral degree in either: a data science related field (e.g., data science, computer science, bioinformatics) or public health (e.g., toxicology) with demonstrated practical experience in data science.

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 background investigation.

Preferred Qualifications

Preferred Candidates will have at least two years of related research experience upon earning their doctoral degree with research, leadership, and training necessary to develop and apply machine learning [ML], natural language processing [NLP], use of large language models [LLMs] for processing and extracting information from unstructured text. Ideal candidate will have experience working with biomedical data, reviews, and health effects evaluations with research, leadership, and training necessary to develop strategies for the conduct and management of complex projects. The candidate must be recognized as a subject matter expert (e.g., as evidenced by honors, awards, invited presentations and consultations, contributions to public code or libraries, cited repositories, elected positions in 3 professional societies, journal editorial positions, invited reviewer for journals, publications). The candidate must have strong oral and written communication skills, as well as excellent interpersonal and facilitation skills. The candidate must be able to innovate and work in a team-based, collaborative, and multi-disciplinary organization, in addition to providing advice and consultation to both technical and non-technical personnel.

Benefits

Salary will be commensurate with experience. A full civil service package of benefits may be available. This position includes full Federal benefits, including participation in the Federal Employees Retirement System, a Thrift Savings Plan (401(k) equivalent), health, and life insurance, and paid sick and annual leave.

Equal Employment Opportunity

Selection for this position will be based solely on merit, with no discrimination for non-merit reasons such as race, color, religion, gender, sexual orientation, national origin, political affiliation, marital status, disability, age, or membership or non-membership in an employee organization.

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

Applicants who have completed part or all of their education outside of the United States must provide an evaluation by an accredited organization to ensure its equivalence to education received in accredited educational institutions in the United States. For more information on foreign education verification, visit the National Association of Credential Evaluation Services (NACES) website at http://www.naces.org/. Verification must be received prior to the effective date of the appointment. 4 NIH is the premier biomedical research center for the world. Its 27 institutes and centers employ more than 21,000 employees doing a vast array of jobs, all supporting efforts for a healthy nation. For information about the NIH mission, goals and institutes and centers, visit https://www.nih.gov/aboutnih.

DO NOT INCLUDE YOUR BIRTH DATE, PHOTOGRAPH, OR SOCIAL SECURITY NUMBER (SSN) ON APPLICATION MATERIALS. DHHS AND NIH ARE EQUAL OPPORTUNITY EMPLOYERS

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.

How to Apply

Interested candidates should submit materials as one combined PDF via email to [email protected].

All emails should include vacancy number NR#513 in the subject line.

Applications will be received until position is filled.

A complete application package must include:

• Cover Letter
• Curriculum Vitae
• Bibliography
• Contact information (names, work/email addresses, phone) for 3 References from individuals in the scientific/academic community who are familiar with the candidate’s accomplishments, motivation, and skills.

Incomplete or paper applications will not be accepted or evaluated.

For further information about the position, please contact Dr. Andrew Rooney at [email protected].