The Department of Environmental Medicine and Public Health at the Icahn School of Medicine at Mount Sinai is seeking a full-time biostatistical programmer for epidemiologic studies of environmental exposures and human health. The candidate will be a member of a collaborative research group that uses advanced epidemiologic and computational methods to analyze high-resolution metabolomics and other diverse types of high-dimensional data to study the exposome, a paradigm encompassing the totality of environmental exposures and biological responses through the lifecourse.
The research team is based in a large department that is a leader in environmental public health research within a prominent academic medical campus next to Central Park in Manhattan’s Upper East Side. Mount Sinai offers many opportunities to learn new research skills, and this position may fit a candidate with strong statistical programming skills seeking additional research experience in high-resolution metabolomics, environmental biostatistics, and data science.
The ideal candidate will have strong data analytic skills and experience programming in R and/or Python. Experience with command line tools and high-performance computing is desirable.
• Apply statistical, machine learning, and network-based analyses to analyze metabolomics data and other types of high-dimensional data in epidemiological studies
• Implement high-resolution metabolomics data workflows using R and other tools, including peak picking, data preprocessing, and metabolite identification
• Clean and manage project files, including merging, conversion, and reformatting of diverse data types
• Conduct and document analyses using reusable code and tools for reproducible research (e.g. git, github, knitr)
• Prepare presentation materials, scientific figures, and other rich data summaries
• Assist in manuscript preparation with the possibility of co-authorship on scientific publications
• Review proposed grant applications
• Participate in research group meetings, including presenting summaries of ongoing analyses
• Review and verify code prepared by other research group members
• Remain informed of new developments in statistical programming that relate to the research program
• Perform other related duties