The Center for Statistics and Machine Learning’s DataX Fund supports the hiring of data scientists who will create and improve data-analysis software to operate at large scale, leading to faster discovery, wider impact and greater continuity. These data scientists will work in faculty-led, collaborative, multidisciplinary environments, and leverage their skills to advance the progress of data-driven scientific discovery.
Current Opening: Princeton Computer Science is seeking a Data Scientist to work with its world-renowned faculty and students to solve biomedical research questions using novel computational approaches. The data scientist will develop novel, scalable algorithms and machine learning techniques and apply these to large repositories of biomedical data. They will work with faculty, post-doctoral researchers, and graduate students in research projects across multiple biomedical applications, particularly in human genetics and disease. As a Data Scientist you will work in a collaborative, multidisciplinary environment and actively contribute your skills to advance scientific discovery. You will have access to Princeton's first-class resources, the opportunity to co-author academic publications, to offer short courses and workshops on data science, and to collaborate with the larger data science community. You will join a team of five Data Scientists working across multiple disciplines as part of the Schmidt DataX Project at Princeton, an initiative made possible by a major gift from Schmidt Futures. Appointments are for 3-years and offer a very competitive salary and excellent opportunities for growth and career development. The Biomedical Data Science initiative is spearheaded by the Department of Computer Science, with strong connections to the Lewis-Sigler Institute for Integrative Genomics, Center for Statistics and Machine Learning, and other engineering departments.
- Ph.D. required in computer science, mathematics, statistics, data/computational science, or related disciplinary field or equivalent combination of educational training and relevant experience.
- Strong coding/algorithm prototyping skills, and ability to explain and document work.
- Proficiency in one or more of the following: Python, R, C/C++, or Julia.
- Experience using data analysis, statistics, machine learning, and/or scientific computing to address basic research questions; or commensurate achievements
Questions? Contact Project Manager, Ellen DiPippo
Ready to Apply: Full job description and access to application on Princeton’s job posting system: https://www.princeton.edu/acad-positions/position/19701.