- Job Name
- Data Scientist
- Posting Date
- 07/09/2019
- Brief Description
- In collaboration with campus stakeholders and IRPA staff, the Data Scientist will be responsible for the implementation of on-going academic, co-curricular, and institutional assessment efforts. The position will utilize existing institutional data, as well as implement new data collection efforts, to evaluate academic, co-curricular programs, and other initiatives across the university. It will work with campus stakeholders to evaluate programs using both statistical methods and predictive analytic tools. It will help implement predictive analytic tools on campus, and assist in building campus expertise with those tools through training and online support. Information developed through these efforts will inform senior campus leaders. Additionally, the Data Scientist is part of the IRPA team engaged in analytics and data modernization, providing advice and expertise on modern technologies and decision-support platforms.
- Job Category
- Institutional Research
- Job Type
- Full-Time
- Education Level Required
- Masters
- Location City
- College Park, MD 20742, United states
- How to Apply
- Applications must include the following in a PDF format:
• Letter of interest
• Resume with current mailing address
Submit application materials to:
Michelle Appel, Search Chair
Via upload at www.ejobs.umd.edu
Staff position #102374
For best consideration, please submit application materials by July 24, 2019. Position will remain open until filled.
- Direct Link
- Click here for more info
- Job Details
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Job Qualifications:
· Master’s degree in Mathematics, Statistics, Operations Research, Economics, Information Systems, Data Science or Analytics, Social Sciences, or related quantitative field required, Ph.D. preferred.
· At least two years of full-time (or part-time equivalent) in a quantitative field with a focus on applied analytics and/or predictive modeling required.
· Experience querying databases and large data sets to combine and manipulate data (e.g., import data, assess missingness, filter, transform variables, derive variables) using data extraction tools.
· Experience applying predictive methods along with extensive knowledge of statistical theory and data mining techniques (e.g., GLM/regression, statistical tests, non-parametric statistics, clustering, factor analysis). Experience using statistical software packages and languages.
· Demonstrated ability to communicate advanced analytical concepts and complex quantitative analysis in a concise, clear, and actionable manner. Demonstrated ability to tell stories with data using data visualization software or other reporting/BI tools.
· Strong initiative and a resourceful approach to problem solving and learning. Ability to work independently and as part of a team in a fast-paced environment. Strong critical thinking, analytical and organizational skills. A drive to learn and master new techniques and technologies.
· US Citizenship or Permanent Residency.
The following are preferred:
· Ph.D preferred.
· Experience with machine learning algorithms (e.g., random forest, simulation, text mining, decision trees, neural networks).
· Knowledge and experience working with the following data extraction tools: SQL, SAS Enterprise Guide, Tableau Prep.
· Knowledge and experience working with the following data visualization tools: SAS Visual Analytics, Tableau.
· Knowledge and experience working with the following statistical software packages and languages: SAS, SPSS, Stata, R, and/or Python.
· Experience in higher education (e.g., institutional research, educational research) and with student and faculty information.
· Experience providing guidance and oversight to junior analysts on technical aspects of their work outside of formal project assignments.
· Demonstrated experience with program evaluation and report writing.
Salary commensurate with experience and qualifications; range starts at $87,400. Excellent leave, medical coverage, retirement, and tuition-remission benefits. The position is available immediately.