Uses machine learning techniques to develop predictive models (including but not limited to image data).
Manipulates and analyzes geospatial data.
Programs in at least one of the C-family of languages (e.g., C/C++, Java, etc.).
Cleans, transforms, merges, and matches between large and complex research and administrative datasets. Plans own resources to collect, organize, and analyze information from the University's various internal data systems as well as from external sources.
Builds and analyzes statistical models and reproducible data processing pipelines using knowledge of best practices in machine learning and statistical inference. Serves as a single point of contact for all requests and engages other IT resources to assist as needed. May partner with other campus teams to assist faculty with data science related needs.
Performs other related work as needed.
Masterâ™s degree or higher in data science, statistics or computer science.
Courses in linear and non-linear (e.g., logistic, Poisson, survival, etc.) regression models, as well as in other specialized statistical areas (e.g., spatial regression analysis, multi-level models, econometric models).
Working knowledge in an academic research environment.
Background working with health care claims data, especially Medicare and/or Medicaid data.
Proficient in manipulating and analyzing scientific survey data.
Strong Python skills, including use of Pandas, Dask, machine learning (e.g., with TensorFlow or PyTorch) and web-based application/API development.
Exceptional skills in the use of high-performance and cloud computing for data manipulation and analysis, including Googleâ™s Compute Engine and AWS as well as software frameworks such as Apache Hadoop or Spark.
Accomplished statistical skills including model building, interpretation, treatment of missing data, and use of simulation to explore model properties.
Skillful in manipulating and analyzing geospatial data, including experience with R (geospatial), ArcGIS, QGIS.
Good writing skills, including both technical documentation and user guides.
Knowledge of data visualization principles and related software (especially in Python).
Solid working knowledge of other software packages used for data manipulation and analysis (e.g., R, Stata, Julia).
Outstanding Unix/Linux skills.
Cover Letter (required)
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