Department or Employer: Office of Institutional Research and Assessment
Location: UNC at Chapel Hill
Project or Group Web Site: https://unc.peopleadmin.com/postings/174946
Description:
The Data Scientist will play a key role in supporting data-informed decision-making, planning, and continuous improvement at UNC-Chapel Hill by generating insights from institutional data through the application of advanced analytics and data science techniques.
The position will be responsible for translating data on students, instruction, academic programs, support services, operations, human resources, finances, facilities, research administration, and other aspects of the enterprise into actionable information that informs policy, practice, and achievement of the University’s strategic objectives. The primary responsibilities of the Data Scientist generally fall into the three major areas of effort: 1) generating knowledge through extensive data mining and analysis, 2) communicating results of these analyses in visual and other formats that provide strategic intelligence for a wide range of campus stakeholders, and 3) serving as major contributor, technical resource, and trainer to the University’s Data Analytics team and overall data analytics community.
Application deadline: February 4, 2020
Educational Requirements
Master’s and 5+ years of experience.
Qualifications and Experience
Master’s or doctoral degree in Data Science, Statistics, Mathematics, Engineering, Operations Research, Computer Science, Informatics, Economics, Psychology, or related quantitative fields.
At least five years of full-time post-graduate work experience managing and executing data analytics projects in academic, non-profit, or for-profit organizations.
Experience carrying out the full range of data analytics project activities including data cleaning, modeling, analyzing, mining, and synthesizing large, complex data sets to produce informative reports and presentations.
Experience using a database management language such as SQL.
Expertise in conducting advanced statistical analysis including regression analysis, factoring, clustering, decision trees, experimental design using data analysis software such as SAS, Python, SPSS, R, etc.
Experience building and testing predictive models.
Demonstrated ability to work collaboratively and productively in diverse, cross-functional teams that include researchers, IT professionals, subject matter experts, and other stakeholders.
Demonstrated expertise in using data visualization software and tools to create professional quality visualizations, dashboards, interactive reports, and other data products to be used by decision makers at all levels
Preferred Knowledge, Skills, and Abilities
Advanced knowledge of relevant commercial and open-source software such as SAS (Base, Enterprise Miner, Enterprise Guide, Visual Analytics, Visual Statistics, or Viya), SPSS, R, Tableau, MS Excel, Python, etc.
Rich experience in data modeling and advanced statistical techniques including probability and statistical models, forecasting techniques, time series and trend analysis, regression, analysis of variance and multivariate analysis, factor analysis, etc.
Extensive experience developing predictive models and utilizing machine learning/data science technique such as Hierarchical Clustering, Regression, Artificial Neural Networks, Decision Trees, Random Forest, Na?ve Bayes, etc.
Experience in building, training, scoring, tuning, and maintaining predictive models
Ability to clearly communicate and accurately present methodology and statistical results to various technical and non-technical internal audiences.
Adept in agile methodologies.
Date Posted: 10 Jan 2020