The Data Scientist will work closely with faculty and researchers at the Department of Public Health Sciences and the Center for Data Intensive Science at the University of Chicago to obtain, manage and analyze data for several projects related to transportation systems and health policy. Examples include but are not limited to a project that aims to assess population level physical activity (bicycling and walking) in developing countries through the analysis of street and satellite imagery, and the analysis of in-vehicle telematics data to assess how road infrastructure affects driver behavior.
This at-will position is wholly or partially funded by extramural funds (e.g., grant, gift, endowment) which are renewed under provisions set by the grantor. Employment will be contingent upon the continued receipt of these extramural funds and satisfactory job performance. If this position is eliminated due to the discontinuation of extramural funding a minimum of one pay period's written notice will be given (If exempt: 30 days, If non-exempt: 2 weeks), or pay in-lieu of notice.
Compile, organize and summarize large datasets, deriving new data products, and preparing datasets for specific analyses.
Perform statistical analyses based on the individual's interest and skills.
Design and implement procedures for data management and analysis that are both efficient and reproducible, and for writing corresponding documentation.
Knowledge in relevant scientific field required.
Knowledge of research techniques or methods required.
Knowledge of regulatory policies and procedures required.
Analytical skills required.
Problem-solving skills required.
Attention to detail required.
Organizational skills required.
Verbal and written communication skills required.
Ability to work independently and as part of a team required.
Ability to learn new research techniques or methods.
Education and Experience:
Required: Masterâ™s degree in computer science or data analytics, with coursework in machine learning and in data analysis including spatial data analysis, predictive analytics and data mining.
Preferred: Coursework in one or more statistical topics including (but not limited to) regression analysis, non-linear models, spatial data analysis, and time series analysis.
Coursework in methods and tools for analyzing large datasets (e.g., parallel computing, Hadoop, Apache Spark/Storm, etc.).
Required: Three years or more programming experience with at least one or more years in the manipulation and analysis of quantitative research data within an academic environment.
Experience using cloud-based platforms and services (e.g., Google Cloud, AWS) and working with open source software projects. Solid knowledge of Python, at least one C-family language, and at least one data analysis environment (e.g., R, Stata, SciPy, etc.).
Experience developing web-based applications and using relational and/or non-relational databases. Experience working with GIS software.
Preferred: Experience in using data analytics for transportation systems (e.g., large scale image processing for vehicle detection) and/or health data (e.g., insurance claims data).
NOTE: When applying, all required documents MUST be uploaded under the Resume/CV section of the application.
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