Working Group Lead: Tyler McCormick


How do we create and sustain long-term career trajectories for a new generation of scientists whose research depends crucially on the analysis of massive, noisy, and/or complex data and whose work may require substantial curation and/or development efforts? What about those scientists who focus entirely on building next-generation tools that others ultimately use to derive new science? These career paths are currently complicated by strong competition from industry, a tendency by universities to measure productivity using only traditional metrics (e.g., journal publication) that do not reflect the scholar’s full contribution, and a general failure to provide a sufficiently supportive and meaningful environment and culture.

We see persistent, complex, and deep-rooted challenges for the career paths of people whose skills, activities, and work patterns do not fit neatly into the roles and success metrics of traditional academia. However, data science in research universities requires precisely the kind of complex, long-term interdisciplinary work with methodological and engineering efforts that leads to “low performance” under traditional metrics and “slow progress” and “lack of fit” in existing career tracks.

Activities around this theme in the Data Science Environments include the following:

  • Defining positions and career tracks (e.g., data science faculty lines, data science fellows)
  • Promoting institutional policies that facilitate career development for data scientists
  • Building a recognizable data science community across scientific domains
  • Promoting mentorship and job satisfaction