Data Manager

This is a grant funded position. We are seeking a talented and motivated Data Manager to contribute to groundbreaking projects funded by the National Institutes of Health (NIH) and the Veterans Affairs (VA). As a key member of our team, you will contribute to trans-ancestry genetic studies on major depression, bipolar disorder, schizophrenia, and substance use disorders. This role involves managing large-scale, complex datasets, coordinating data collection and integration, and ensuring data quality and integrity, all while implementing best practices in data storage, security, and version control. Key responsibilities include overseeing data handling, processing, and quality control across our research projects. The ideal candidate will have experience working with diverse datasets, including genomic, phenotypic, and social determinants of health data, and possess strong proficiency in data science tools for data wrangling, cleaning, and visualization. Familiarity with cloud computing platforms (e.g., AWS, Google Cloud) and version control systems (e.g., GitHub) is essential for managing data workflows efficiently and ensuring reproducibility. Strong problem-solving abilities, meticulous attention to detail, and a commitment to data accuracy and reproducibility are critical to success in this role. The candidate will have the opportunity to join the Institute of Genomics in Health (IGH), the Population Genetics and Environment in Major Depression (POP-GEM) Working Group and engage in the PsycheMERGE Diversity Initiative and collaborate with groups like the Psychiatric Genomics Consortium (PGC) and the Latin American Genomics Consortium (PGC). The appointed candidate will participate in team science with IGH faculty including Drs. Roseann E. Peterson, Tim Bigdeli, Jacquelyn Meyers, Peter Barr, and Chris Chatzinakos., REQUIRED: Data Scientist position in Psychiatric and Statistical Genetics. Applicants should have a PhD, MD or MD/PhD and a background in one of the following fields: human genetics, behavioral genetics, biostatistics, mathematics, statistics, computational biology, bioinformatics, data science, computer programming, or similar. PREFERRED: Strong candidates will have excellent programming and data analytic skills, knowledge of statistics, familiarity with genome informatics tools, and a strong publication record is preferred, but interested applicants are encouraged to apply regardless of past training experiences.