Image Credit: Getty Images via Inc.com
This time we would like to take a look at how capabilities of data scientists have evolved and how virtues come into the picture, literally.
In the mid-2000s, after graduating from university, a young man was working in Washington, DC in the defense and intelligence communities, focusing on applying computational and statistical methods to social dynamics and organization. A few years later in 2008, right around the time DJ Patil and Jeff Hammerbacher coined the title Data Scientist, the same young man started pursuing a PhD in Political Science and went on to pioneer the art of applying computer science tools to social science. He later introduced a famous Data Science Venn Diagram.
By now, you might have figured out that we are talking about Drew Conway. The Venn diagram he introduced in 2010 clarified three pillars for success in data science: Math & Statistics Knowledge, Hacking Skills, and Substantive Expertise.
Figure 3-1: Data Science Venn Diagram 2010 (source)
What has Evolved a Decade After Inception?
While much of the 2010 original terms and ideas are still valid, there have been updates, debates and even battles on the Data Science Venn Diagram: a simple image search of these words would yield scores of variations.
To us, it is expected that the concepts have expanded greatly since inception. In 2020, a data scientist’s technology capabilities have broadened to beyond math and statistical knowledge, to include the mapping of scenario to appropriate to optimization techniques, taking care of intricate data nuances when building models to discovering patterns, and the careful evaluation of solution success.
A data scientist’s execution capabilities goes beyond hacking skills, to include architecting and mapping of complex problems into a sequence of machine learnable tasks, robustly maintain a high quality of work, and perform independently with little or no guidance, while understanding when to solicit necessary input.
Substantive expertise can be clarified into having domain knowledge of the organization’s objectives, understanding data biases and limitations in an industry, as well as the industry’s operational dynamics.
Having built data science teams in companies private and public, large and small, and after interviewing hundreds of data science candidates in the US and China, we discovered, as expected, that data scientists who are strong in all three capability dimensions are indeed quite difficult, if not impossible, to find.
A data scientist entering the field of data science with academic and industry internship background, such as Aayana from last week’s post, usually has strong capabilities in the technology dimension.
A data scientist with years of experience in industry can usually pick up the second dimension of skills in execution, which includes the ability to deploy scalable and maintainable data science solutions.
A seasoned data science practitioner who also has the third dimension of domain knowledge in a related industry would be highly valuable to any employer.
But What is Missing?
As with any teams and companies, we had our share of blind spots in building teams. While we dutifully scored candidates in their technology, execution, and domain knowledge abilities, our hiring mishaps show up as candidates get vetoed in final-round executive interviews, or worse, get hired and then have to be managed out.
Many of these failures were summarized as “not a cultural-fit”. But what does that mean? What is the “culture” for the data science discipline that we are looking to fit? How is that distinct from an organization’s culture or an industry’s culture?
To analyze these failures, we propose to expand the interviews and evaluations of a data scientist to consider not just the capabilities, but also the virtues of a data scientist in pursuing a data science career.
The Virtues of a Data Scientist
For virtues, we highlight three dimensions: Ethics, Rigor, and Attitude. We found that when data scientists maintain good practices in these three dimensions, they are more likely to produce significant impact for their organizations. On the other hand, when data scientists neglect one or more of these dimensions, it can lead them into difficult situations where they must be mentored or in some cases be managed out.
In defining the Virtues of a Data Scientist, we propose the following:
Ethics: Ethics are standards of conduct at work that enable data scientists to avoid unnecessary and self-produced breakdowns. There are many aspects of work ethics for data scientists, including data use, project execution, and teamwork.
Rigor: Rigor means producing results that are “scientific”, which are repeatable, testable, and discoverable, where the work product can be used to reliably anticipate future situations.
Attitude: Attitudes are the moods with which a data scientist approaches the situations that arise in the workplace. When left unchecked, they often reflect the instinctive reflex of the situation at hand, which may not produce the most desirable outcome.
There you have it: we have effectively transformed what was a Venn diagram from the early days of Data Science, to the TED-ERA Fan.
Figure 3-2: The TED-ERA Fan
In the near future we will introduce another Fan (the LOOP Fan) that brings the Capabilities and Virtues into action in pursuing an impactful career. For now and in the next few posts, we will focus on each part of this Capabilities-Virtues fan, with examples, interpretations, and suggested guidelines.
If you have views, questions, or examples you would like to share, feel free to comment below or contact us directly.
About the authors:
Dr. Jike Chong is a director of data science at LinkedIn, leading a team to develop products to advance people’s careers in the context of the hiring marketplace. He previously built and grew several high-performing data science teams at Acorns, Yirendai (NYSE:YRD), and Simply Hired. Dr Chong also advises several startups and teaches at Tsinghua University.
Yue Cathy Chang is an executive leader in business development, sales and marketing for data products and services. Cathy has held strategic roles serving customers using data science- and engineering-based solutions at bluechip software enterprise companies and startups. Well-versed in tech and business, she has frequently spoken at data and AI events.
The writing and opinions expressed are solely our own and are not shared, supported, or endorsed in any manner by our employers.
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