Blog Podcast 3 Min Read

Recruiting and Building the Data Science Team at Etsy

Blog Podcast 3 Min Read
By: Sara Petrie
Posted on: June 16, 2021

Data+AI Battlescars Takeaways: Recruiting and Building the Data Science Team at Etsy

Chu-Cheng HeadshotIn this episode of Data+AI Battlescars (formerly CDO Battlescars), Sandeep Uttamchandani talks to Chu-Cheng, CDO at Etsy. This episode focuses on Chu-Cheng’s battlescars related to recruiting and building a data science team.

Chu-Cheng leads the global data organization at Etsy. He’s responsible for data science, AI innovation, machine learning and data infrastructure. Prior to Etsy, Chu-Cheng has led various data roles, including at Amazon, Intuit, Rakuten and eBay. Here are the key talking points from their chat.

Building a Data Science Team: The Early Stages

  • At the early stages of building a data science team, it may be more useful to hire people who are generalists rather than, for example, specialized data scientists or machine learning engineers.
  • In any successful team, you need a mix of different experience levels and skill sets.
  • When building a data science team, Chu-Cheng actually looks for people who probably wouldn’t pass a traditional data science interview, but can still get the job done.
  • For first hires, Chu-Cheng generally targets people who have at least a few years of work experience and have a lot of patience and willingness to learn.
  • A good candidate is someone who can explain a difficult concept in a way that anyone can understand. You want to find someone who knows how to tell people what they are thinking.
  • For example, you can follow them on LinkedIn and see their profile, how they write their own self description, and you get an idea even before the interview.
  • In the past, Chu-Cheng often unconsciously looked for someone with whom he had a similar background.
  • To counteract this bias, he learned to create a checklist of criteria prior to interviewing a candidate. He uses that checklist to evaluate the candidate’s qualifications, rather than just picking someone who has a similar background to him.

Building a Data Science Team: The Later Stages

  • Eventually when you have a bigger team, you must start moving from hiring generalists to hiring specialists. If you only hire generalists, you’ll eventually run into a wall, because you have a bunch of people who are fungible.
  • In interviews, Chu-Cheng tries to balance technical and soft skills, even when hiring scientists and engineers.
  • If you are interviewing someone for a manager position, prioritize their mentoring, coaching, conflict resolution, and communication skills. The manager’s success is defined by the team’s success.
  • As a manager, when coaching someone, instead of trying to give out or prescribe an answer, focus on how you can ask the right questions so that the person can come up with a solution on their own. Switch from telling to asking. Allow people to make mistakes so you can coach them to grow and learn a lesson from it.

Innovation at Etsy

  • Chu-Cheng tries to teach his team how to write papers and patents. At Etsy, they encourage this innovation by sending a recognition gift or an innovation award.
  • Papers and patents are not the only types of innovation, however. Innovation also involves the process of making something easier. Not everything can be patented or become a paper, but you can, for instance, write a blog sharing your learning. Innovation is a mindset.
  • When considering a new technology, it is important to get a sense of the circumstances under which you should not use the technology, as well as when to use it. You must know when to use what.

If you’re interested in any of the topics discussed here, Sandeep and Chu-Cheng talked about even more in the full podcast. Be sure to check out the full episode and the rest of the Data+AI Battlescars podcast series!