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This time we would like to share a real-life scenario on the early part of the career spectrum and begin discussing what makes a successful data scientist.
Interview Woes
Aayana is a graduate student studying computer science at UCLA. Her easy-going nature belies the rigor she gives to her technical work. Since arriving in the US from India 15 months ago, she already took several advanced courses in machine learning, worked with two prestigious research groups, and interned at a fast-growing, soon-to-IPO, Silicon Valley startup company.
With graduation coming up in six months, as with many ambitious young professionals, she began interviewing for a full-time position.
What she encountered in the interview process for data science positions brought her confusion and frustration. She lost her usual confidence and was desperately seeking help from mentors.
What happened?
It turns out that each data science interview Aayana encountered was pretty different.
The interview for a natural language processing (NLP) engineering position started with an informal chat about her prior projects.
Another interview in a FinTech startup started with a task to write down an entire data pipeline. Aayana was given information on the sparsity of data and the available sample size, and was asked to suggest the best algorithm to use. Then, she was asked to improve the data pipeline and better process the raw data for the final model.
These interviews looked very different from the machine learning course exams she excelled at just weeks earlier. With vastly different starting points for the data science interview processes, she was at a loss on what to expect.
Aayana was confused and frustrated about not knowing whether there is a standard process for data science interviews, or if every company and every team would have a different set of criteria they are hiring for.
What are Hiring Managers Looking For?
According to LinkedIn Talent Insight data, only 20% of data scientists work in companies with 100 data scientists or more. In large companies, there are often mature processes for interviewing, evaluating, and promoting career growth in the domain of data science. These large companies represent all but 0.25% of all companies employing data scientists in 2020.
The vast majority (67%) of data scientists work in companies with data science teams of fewer than 30 members, representing 99% of the companies employing data scientists.
The field of data science is still nascent. Interview questions for entry-level data scientists are highly dependent on the company stage, product maturity, and the skill sets hiring managers are looking for to complete their team.
Fundamentally, however, for any team to perform, what all hiring managers look for fall under two areas: ability and motivation.
Understanding Ability and Motivation: Assessing Capabilities and Virtues
For a data science team, ability is the capability to produce accomplishments using data science skills. It is the hard technical skills that can be learned and accumulated over time.
Motivation, on the other hand, is the willingness to work together, within the team and across teams. It is the virtues or the set of soft skills we accumulate over time to harmoniously produce accomplishments together for the organizations we are a part of.
For Aayana, coming from an academic environment, the assessment of capabilities in interviews is more familiar. On the other hand, the assessment of virtues intertwined in the interviews can take conversations into many unfamiliar territories - generating confusion and frustration. However, if Aayana understood what fundamental areas interviewers are looking to assess, she would be a lot more confident walking into her interviews.
In the upcoming posts, we will discuss dimensions of Capabilities and Virtues and the pursuit of an impactful career in data science, and suggest ways we can practice capabilities and cultivate virtues over time.
As a preview, we will assess capabilities in these dimensions: Technology, Execution, and Domain knowledge, and Virtue in these dimensions: Ethics, Rigor, and Attitude.
What may be included in each of the dimensions for data science Capabilities and Virtues? For different milestones of a career? In different size companies? And in different industries?
Our upcoming posts will offer interpretations across different spectrums of a career in data science.
If you have 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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