Please distribute the ad below to qualified candidates who want to continue their career educating and training collaborative statisticians and data scientists.
Official job ad:
https://jobs.colorado.edu/jobs/JobDetail/?jobId=26269We seek to hire a post-doctoral researcher to engage in collaborative research with domain experts across campus helping them apply statistics and data science to answer their research questions, help develop foundational research in the micro- and macro-theory of collaboration for statistics and data science, mentor graduate and undergraduate students in interdisciplinary collaboration, and teach one statistics/data science course per semester. Successful candidates will sustain a passion for increasing the extraordinary impact of statistics and data science by collaborating on projects that benefit society.
The post-doctoral researcher will work closely with Dr. Eric Vance in LISA, the Laboratory for Interdisciplinary Statistical Analysis within the University of Colorado Boulder's Department of Applied Mathematics. LISA is a statistics and data science collaboration laboratory that A) trains students to become effective interdisciplinary collaborators, B) provides research infrastructure for the campus community to enable and accelerate data-driven research and decision-making, and C) teaches short courses and workshops to improve statistical skills and data literacy widely. The post-doctoral researcher will also have opportunities to engage with the LISA 2020 Program, a global network of statistics and data science collaboration laboratories building capacity to transform evidence into action for development worldwide.
The post-doctoral researcher will initially receive intensive training in the micro- and macro-theory of statistical collaboration and will assist and then lead the education and training of students in effective interdisciplinary collaboration.
Required qualifications:
- PhD in statistics, data science, or related field (conferred by August 2020)
- Experience collaborating with domain experts from a variety of disciplines
- TA or teaching experience
Preferred qualifications:
- Experience consulting/collaborating on at least 20 projects in which the candidate worked with domain experts to apply statistics/data science to answer their research questions or make data-driven business or policy decisions
- Experience mentoring others in statistics/data science consulting or collaboration
This position is for one-year starting January 2021 and is expected to be renewed for 2 more years (3 years total). Apply at
https://jobs.colorado.edu/jobs/JobDetail/?jobId=26269Brief description of the TETRIDS project: IGE: Transforming the Education and Training of Interdisciplinary Data Scientists (TETRIDS)The capability to effectively analyze data to transform numbers into action is increasingly important in today's society. Academic researchers collect and analyze data to advance scientific knowledge. Businesses and policy use data to make business or policy decisions. The nation needs to educate and train more students with deep technical skills in data science and broad interdisciplinary collaboration skills to work with these researchers, businesses, and policy makers to convert data into benefits for society. This National Science Foundation Innovations in Graduate Education (IGE) award to the University of Colorado Boulder will test the effectiveness of an innovative program to educate and train interdisciplinary data scientists who can move between theory and practice to solve problems for real-world impact. This program combines innovative classroom instruction and learning activities in the theory of interdisciplinary collaboration with practical data science experience working on real projects in the Laboratory for Interdisciplinary Statistical Analysis (LISA). Specifically, graduate students learn to adopt effective attitudes of collaboration, how to structure effective meetings with domain experts, what to focus on to make deep contributions to the domain, effective communication skills such as asking great questions, and how to cultivate strong relationships with their collaborators. At the same time, students put this knowledge into practice by collaborating with researchers, businesses, and policy makers to apply data science to solve problems and make decisions. Ultimately, this program may lead to a transformation in who can be trained to become data scientists, where they can be trained, and what can be achieved when we transform data into societal benefits.
The goal of this IGE project is to evaluate how effectively the LISA program educates and trains graduate students from a variety of backgrounds to become collaborative data scientists. Specifically, this project will combine assessments of students' technical skills, student self-evaluation surveys, LISA administrative records, domain expert feedback surveys, and independent expert evaluations of students' projects to answer two research questions:
To what extent are LISA students effective interdisciplinary data science collaborators? (I.e., how well do they do on their projects compared to historical norms, other cohorts of students, and an expert collaborative data scientist?)
What degree of technical preparation in data science is sufficient for graduate students to become effective interdisciplinary data science collaborators?
Knowledge generated from this project may inform best practices across colleges and universities for thousands of graduate students to be educated and trained to become effective interdisciplinary data scientists.
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Eric Vance
LISA, University of Colorado-Boulder
Associate Professor and Director
Boulder, United States
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