We are excited to release the Career Paths and Prospects in Academic Data Science: Report of the Moore-Sloan Data Science Environments (MSDSE) Survey. The survey and report are a joint collaboration between researchers at the Berkeley Institute for Data Science at UC-Berkeley, the eScience Institute at UW-Seattle, and the Center for Data Science at New York University. These three institutes are funded by the Gordon and Betty Moore and Alfred P. Sloan Foundations to support data-intensive research across fields. In this project, we surveyed 167 researchers who were affiliated with these three institutes for data science, with our respondents spanning many fields, roles, and career stages.
The term “data science” is increasingly used within academia to refer to a broad set of cross-disciplinary methods and collaborations around data and computation. It is variously defined and institutionalized as an emerging interdisciplinary field, scientific paradigm, or intersection of new and old common concerns. Our motivation is to study how those who practice and support data- and computational-intensive research fit in the formal and informal organizational structures of academia. Our report discusses many issues around academic data science through the lens of career paths. We provide insights, recommendations, and questions for those practicing, supporting, or institutionalizing data science in academia.
Just as there is no single definition of data science, there is no single model of what a data scientist in academia does, although we identify major themes and clusters. People have different definitions of data science in part because they have different ideas about what is important in working with data at scale, as well as different ideas about what roles people with these skills should play in academia. Academic data scientists typically have substantial specialized expertise in working with data at scale across a complex research pipeline, developing and maintaining software tools and research infrastructure, doing research in an open and reproducible manner, and integrating computational, statistical, and subject matter expertise. Many academic data scientists also often teach workshops that fill gaps in the traditional university curriculum, provide research consulting to help support researchers across domains, and mentor students across fields who find themselves in similar kinds of positions.
Many early career researchers in this interdisciplinary space are making substantial and broad contributions to the research, teaching, and service missions of universities. However, these are often not recognized or incentivized by traditional disciplinary institutions, like faculty hiring, tenure, and promotion committees. This is particularly the case for data scientists whose primary fields are in the physical, life, and social sciences -- as a majority of our respondents are. There can be substantial ambiguity and uncertainty among early career researchers about whether these kinds of contributions will pay off in their careers.
Contrary to the common narrative that data scientists are leaving academia because of high-paid salaries in industry, we find a common lack of recognition and reward structures, particularly as early career researchers receive PhDs and transition into ambiguous postdoc and research staff roles. The graduate students, postdocs, and research staff with specialized data science expertise are often highly valued in more junior and support roles, but long-term career paths are often lacking. When asked to rank how beneficial or detrimental various activities are for tenure in their field, the kinds of activities that distinguish data scientists generally rank below research publications in one’s field, teaching courses, and even traditional academic service.
Our research also shows that there is no one-size-fits-all solution to creating fulfilling, sustainable career paths for academic data scientists, as our respondents often had different career goals and priorities. Some deeply value autonomy in research and want to be principal investigators on their own grants, while others see themselves closer to research consultants or infrastructure support. Data scientists place different importance on factors like having influence on the direction of their university, lifetime/tenured employment, or a highly respected professional title. It is important to support data scientists seeking traditional faculty roles, as well as those in the “alt-fac” trajectory. Finally, it is important to provide early career researchers with support, guidance, and professional development so that they can know what various career paths look like in and out of academia. We provide composite portraits of individuals who are in several of these different situations, in collaboration with a broader interview and ethnographic-based project on data science in academia.
We invite you to read the full report and add your voice to this conversation. Our survey was limited in scope and distribution, and we would love to hear from you about how you think data scientists are and are not fitting into the disciplines, structures, and institutions of academia.
Career Paths and Prospects in Academic Data Science: Report of the Moore-Sloan Data Science Environments Survey
R. Geiger, Charlotte Mazel-Cabasse, Chihoko Cullens, Laura Norén, Brittany Fiore-Gartland, Diya Das, Henry Brady
June 6, 2018 | SocArXiv