Career Paths and Alternative Metrics

Working Group

The current system for career advancement in research universities, which is heavily weighted toward publication, often does not align with what makes a modern data scientist successful. Scholars who fail to satisfy the demand for a high volume of publications in respected disciplinary journals find it extremely difficult to earn stable career paths, peer recognition, and institutional support (financial and otherwise). However, data science in research universities requires the kind of complex, long-term interdisciplinary work with methodological and engineering effort that leads to “low performance” under traditional metrics and “slow progress” and “lack of fit” in existing career tracks. If we are to build successful environments for data science, we must overcome these deficiencies.

This working group aims at identifying and promoting alternative metrics and career paths that lead to growth and advancement opportunities for scientists who do not fit the typical academic mold but are critical to its success. We seek to create a diverse set of persisting and motivating career paths for data scientists inside the academic environment in order to engender the most productive, innovative, and deeply invested data science community.

In partnership with our partners at the University of Washington and New York University, we will tackle a series of cross-institutional barriers to career advancement for data scientists, including the lack of institutional support, data scientist autonomy and respect, publication venues and alternative metrics to recognize contributions, and clear roles and mentorship.

We will conduct a variety of career path experiments targeting these barriers and our common goals. We will work closely with the Evaluation and Ethnography Working Group to qualitatively and quantitatively measure the success of these experiments. By comparing and contrasting our results across institutions and domains, we will develop a set of best practices for instilling change in data science career paths, which we will disseminate to a national audience.

 

Working Group Members

Leader

Henry E. Brady

Goldman School of Public Policy
Co-I for Moore/Sloan Data Science Environment

Diya Das

Molecular & Cell Biology

Charlotte Cabasse

Ethnographer

R. Stuart Geiger

Berkeley Institute for Data Science
Ethnographer