Reflection on Faculty Hiring in Data Science
August 21, 2023
I was privileged to attend the annual 2023 Data Science Leadership Summit which took place in May at the Center for Computing and Data Sciences at Boston University. The meeting convened a group of leaders in the field of data science representing a variety of organizations from academia, government, non-profit, and industry. Among the participants were faculty members from community colleges, liberal arts schools, doctorate-granting universities, and minority-serving institutions.
The Summit included a variety of sessions and panels, all of which were very interesting and highly interactive. The organization team and planning committee took care of all the logistical details, a fact that ensured the smoothness of the summit’s operations. Among such sessions, I want to highlight “Challenges in Faculty Hiring”, led by Jeff Hamrick (University of San Francisco) and Kristin Eschenfelder (University of Wisconsin-Madison). Below, I summarize the main points discussed therein and explain some of the background entailed.
Faculty hiring in data science can be challenging due to several factors. One of the main challenges is competition, as there is a high demand for data science faculty and thus national universities and colleges are competing for a limited pool of qualified candidates. Another challenge is the interdisciplinary nature of data science, which requires expertise in statistics, computer science, and domain-specific knowledge. Finding candidates who have expertise in all of these areas can be tough, and departments have not figured out any significant hacks to this issue.
Data science is a rapidly evolving field, and new techniques and technologies are constantly emerging. This in turn makes it difficult for faculty to keep up with the latest developments and incorporate them into their teaching and research, especially as they are trying to juggle all the other academic requirements that a typical school expects. In addition to that, data scientists are in high demand in industry, and many talented candidates, as well as tenured/non-tenured faculty, may choose to work in the private sector rather than academia. This can make it difficult for universities to both attract and retain top talent or simply talent in general. Salary and funding are also challenges in faculty hiring in data science. Data science faculty often command high salaries, and universities struggle to compete with industry salaries.
To address these challenges, universities and colleges need to be creative in their hiring strategies. This may involve partnering with industry to attract top talent, offering competitive salaries and benefits, and providing opportunities for professional development and research funding. It may also involve developing interdisciplinary programs and collaborations to attract candidates with diverse backgrounds and expertise. In conclusion, faculty hiring in data science is a complex process that requires careful consideration of a variety of factors. By understanding the challenges involved and developing effective strategies to address them, universities and colleges can build strong data science programs and attract and retain top talent in the field.
It is worth noting that participants in this session were eager to share their experiences in this realm, both the positives and the negatives, what worked and what did not, which rendered the aspect of the event very collegiate. I have to admit that ADSA did a great job of gathering leaders from different places who are not only passionate about the data science domain, but also willing to share the wealth of knowledge and experiences they have accumulated over the last decade. This is what building a solid community should look like, and ADSA is doing everything possible to facilitate such an endeavor while empowering all sub-communities within the big data science umbrella.