Staff Scientist (Informatics)

The Division of Translational Toxicology's mission is to improve public health by providing the data necessary for regulatory and non-regulatory environmental stakeholders to make critical public health decisions. Thus, we protect human health through our research in epidemiological data, in vivo studies and model systems, in vitro high-throughput screens, and computational approaches. We are now seeking a staff scientist with data science expertise to join the Integrative Health Assessment Branch of the DTT, at NIEHS, North Carolina.

Prospective staff scientists will use informatics approaches and data science methodologies to streamline the identification, assessment, and use of public health data necessary to support DTT's public health assessments and evidence-based decision making. These evaluations will integrate multifaceted published research on the health effects of certain environmental exposures, reflecting the ever-growing complexity of modern environmental science.

Key responsibilities of this position include but are not limited to:

• Provide computational and informatics methodological expertise for IHAB and DTT activities, including systematic reviews and evidence maps, scoping projects, and environmental epidemiological and toxicological studies.
• Identify, develop, implement, and improve informatics approaches, such as the use of artificial intelligence, machine learning, natural language processing, and large language models, for use in systematic reviews, workflows, and tools.
• Conduct research and analysis on the improvement of data and information processing as they relate to workflows in human health effects assessments.
• Advise and inform DTT and NIEHS researchers on the use of data science methods and tools, acting as an advocate for data science within our institution.
• Work closely with other data scientists, computer scientists, and IT technologists at NIEHS
• Lead research projects that advance novel data science methods and resources
• Serve as the technical expert for assessment teams that conduct the evaluations, using DDT and published literature, which inform policy decisions and public health practices (i.e. systematic reviews and evidence maps).
• Function as a liaison between researchers, technical staff, and other data scientists to disseminate the most effective methods for identifying, collecting, analyzing, and interpreting DDT data.
• Represent the DTT and the NIEHS at peer-review meetings, as well as national and international scientific workshops, conferences, and meetings.

Prospective candidates must possess a Ph.D., M.D., Pharm D., or equivalent doctoral degree in data science (data science, computer science, bioinformatics), or otherwise process a public health degree with demonstrated practical experience in data science. Preferably, candidates will have at least two years of relevant postdoctoral research experience in machine learning, natural language processing, and/or the use of large language models for processing and extracting information from unstructured text. Ideally, candidates will have experience working with biomedical data reviews or health-effects evaluations and possess the training necessary to conduct and manage complex research projects.

Candidates will be assess for subject matter expertise as evidenced by honors, awards, presentations, contributions to public libraries, cited repositories, elected positions in professional societies, journal editorial positions, and/or reviewer positions for journal publications. Candidates must have strong oral, written, and interpersonal communication skills, and be able to work in a team-based, collaborative, and multi-disciplinary organization. Skill in advising both technical and non-technical personnel is a plus.

U.S. citizens and permanent residents are eligible to apply. NIH welcomes foreign nationals with the exception of individuals from this list.