Responsibilities may include:

Coding and data manipulation in R, Python or equivalent languages. Optimization and management of bioinformatic workflows for data extraction, processing and annotation of untargeted mass spectrometry data. Apply variable selection techniques based upon univariate and multivariate approaches. Perform EWAS for disease and adverse health outcomes. Database development for creating a cumulative resource of untargeted exposome studies. Organize and manage study meta-data. Establish and manage a FAIR data structure for untargeted mass spectrometry data. Provide summaries and interpretation of untargeted exposo

Our laboratory is located within the Rollins School of Public Health at Emory University. The Rollins School of Public Health is ranked 5th among public health graduate schools by U.S. News & World Report and ranked number 5 in the Blue Ridge Report amongst schools of public health receiving NIH support. The Gangarosa Department of Environmental Health houses researchers with interests spanning Environmental Health, including mechanistic and computational toxicology, genomics, the developmental origins of disease, air pollution, climate change, water, sanitation and hygiene, and environmental epidemiology. Within the department is Emory’s HERCULES Health and Exposome Research Center, an NIEHS P30 Environmental Health Support Center and the first Exposome Research Center in the US. The team science approach in this department provides opportunities to co-author publications with leading collaborators. We offer numerous opportunities for career advancement, including the ability to attend workshops on topics related to the exposome and data science, and travel to national and international conferences. Work-related experience will be considered in determining level and salary.

A master's degree in statistics, biostatistics, computer science, or a related field and three years of experience as a biostatistician, preferably in an academic setting, OR an equivalent combination of education, training, and/or experience. Proficiency in modern high level statistical languages such as S-PLUS or SAS and in database management.

PREFERRED QUALIFICATION:

The ideal candidate must have training in metabolomics, epidemiology, engineering, biostatistics, data science, bioinformatics or other environmental health related fields. Previous experience with big data analysis or omics is preferred. Experience with R or an equivalent programming language is preferred. Strong organizational and time management skills, with the ability to independently meet project timelines is preferred. While preference will be given to candidates with the expertise summarized above, we are willing to train exceptionally motivated individuals with strong analytical and communication skills. Candidates will have hands-on experience working with exposome studies and have the opportunity to learn fundamentals of untargeted mass spectrometry data acquisition and analysis, pre-processing workflows, EWAS and characterization and interpretation of results.