Position Objective: Provide services as a Scientific Data Engineer in support of the overall functions of the National Institute of Environmental Health Sciences (NIEHS) within the National Institutes of Health (NIH). The Predictive Toxicology Branch of the National Institute of Environmental Health Sciences (NIEHS) is seeking an individual to develop methods and software for novel predictive models, including artificial intelligence (AI) and machine learning (ML) applications. This position will work across the Division of Translational Toxicology (DTT) to synthesize toxicoinformatic, spatiotemporal, dose-response, and systems toxicology data sources.
Duties and Responsibilities:
Publish research study results as lead author. 1
Analyze experimental data and interpret results to write reports and summaries of findings. 2
Analyze laboratory findings. 4
Program and use computers to store, process, and analyze data. 3
Facilitate implementation of software tools to be used by the entire Division. - Ad-Hoc.
Build and maintain a repository of shared code for new and ongoing projects. - Quarterly.
Contribute to team-science publications that describe new modeling applications. - Ad-Hoc.
Basic Qualifications:
Master's degree in Information Sciences, Mathematics, Biology, Biochemistry, Biochemical Sciences, or a related discipline.
Bioinformatics training experience.
Experience working with large data sets.
Experience with pipeline development, data quality control, data harmonization, algorithm development, statistical modeling, data cleaning, data analysis, and machine learning.
Experience in clinical and/or translational research.
Skilled in Linux, Java, CSS, Javascript, ArcGIS, PHP, R, Bash, GitHub, and Python.
Minimum Qualifications:
Ability to multi-task and pay close attention to detail.
Excellent analytical, organizational and time management skills.
Strong communication skills, both oral and written.