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The Undervalued Skills Candidates Need to Succeed in Data Science Interviews

December 4, 2020


Lokesh Bisht

After interviewing over a thousand candidates for Data Science roles at DoorDash and only hiring a very small fraction, I have come to realize that any interview process is far from perfect, but there are often strategies to improve one’s chances . Over the course of our interviews, I’ve come across some great candidates who appeared to be brilliant and performed excellently on the technical portion of the interviews, but still did not end up getting the job. 

The most common pattern we see in these candidates is that, while exhibiting strong technical knowledge, they lack the soft skills of communication and critical thinking to solve business problems. These soft skills are an essential element when building highly productive teams, especially in more senior roles, and the limited nature of an interview means that a shaky performance is all the information the interviewer has to go on, potentially dooming a candidate's chances. While different companies vary in their assessment of soft skills during the interview process, at DoorDash, Data Science and Analytics teams spend a significant amount of an interviewee's time in understanding their approach to solving business problems as we actively look for thought leadership in all our candidates.

While problem solving and soft skills aspects of interviews can be a major hurdle, especially given the limited assessment time in an interview, there are some easy fixes that can help candidates overcome these challenges. First we will discuss some of the challenges put in place by the interview process, then we will review the tactics candidates can use to overcome these issues. 

The challenges of interviewing

As much as rejection is a disappointing experience for a highly qualified candidate, it is also disappointing for hiring managers, since it can represent a failure of the recruitment process to identify the best candidates. Every time we reject someone with promising technical skills who did poorly on the communication and problem solving portions we would wonder: Why did the candidate do poorly on the business questions when they clearly were very smart and technically qualified for the role? 

There is a short and a long answer to this conundrum.

Short answer: A bad hire is more expensive than rejecting a good candidate. Someone who struggles to structure their thoughts and express themselves might be  difficult to work with, even if they have great technical skills and experience. 

Long answer: The interview process revolves around the concept of "thin slicing".  At its core, interviewing involves taking a thin slice of a candidate’s experience and combining it with the candidate's potential to figure out if there's a fit for a role. Even with a signal from a few hours of time together, it's nearly impossible to fully appreciate a candidate's capabilities.

The interview process cannot be so elaborate that it turns off candidates, and cannot be so concise that it does not generate any signals of a candidate’s quality. There needs to be a very fine balance of the interview length for the interviewing process to work, which is hard to define as we try to make the interview process as “real” as possible to the actual job. As such, we are left in the middle of combining intuition with a couple of hours of interview conversations to come up with a final assessment. This is the reality of interviewing, which means the onus is often on the candidate to make use of the limited interview time to demonstrate they can problem solve and articulate their thinking. 

Having said that, there are a few things we have seen that successful candidates do to showcase their potential and, through this article, I want to highlight these best practices and how to use social and communication skills to effectively articulate problem solving abilities. 

How to improve interviewing with soft skills 

Emphasize listening: A lot of candidates put too much emphasis on speaking, trying to fill in every silent moment. Silence is absolutely OK. It helps you mentally acclimatize to the interviewing environment and also helps focus on the question/cues the interviewer may be providing. The more a candidate listens, the easier it will be to understand the question from the interviewer’s point of view. This is generally true for all interviews, but even more critical in analytical interviews where you could be designing an experiment, hypothesizing on a feature, or writing complex queries. One of the great signals we look for is the quality and depth of questions that the candidate might ask us, as that highlights that we have been heard and understood.

Show a structured approach to problem solving: The structure with which a problem is approached is generally more important than the final outcome, especially in analytical case studies. This is because a structured response makes it easier for the interviewer to follow the candidate’s train of thought. Remember, the interviewer is not evaluating a candidate on whether they can solve the problem presented during the interview but on their general problem solving methodology.  Let us take an example: 

Interviewer: How do we increase our category share in a new market X?

Candidate: We cannot, because our rival, Y, has taken a dominant position already, or we cannot because of the reasons A, B, and C.

The above exchange may be the right answer, but is that why the interviewer asked that question? No, they want to see how the candidate would approach it, which is not really laid out in their answer. The answer should include both sides of the equation as a means of showing the pros and cons.

Think of everything, but highlight important things first: When answering interview questions, focus on a high content-to-word ratio. Thinking aloud is always a double-edged sword in answering an interview question. It can make the conversation interactive, but can also confuse the interviewer. Clearly call out when thinking aloud, and once done, articulate the summary. Continue using the analysis framework when summarizing. Take an example:

Interviewer: Which metrics would you look at for this problem?

Candidate A: I will look at X, Y, Z. I can also look at A, B, C…. and D, E, F.

Candidate B: I believe X is our true north metric. In addition we should also look at supporting metrics Y, Z. We should look at A, B as our check metrics. There are more things we can look at but these are the important metrics we should start with. 

Candidate B gave a better answer to this question. They communicated their thoughts and understanding of the metric framework, giving a much clearer, thoughtful answer than Candidate A.

Take cues: Interviewers want suitable candidates to succeed, and they will often provide clues to help steer conversations in the right direction. Pay attention to those, and it will be easier to navigate the interview without getting tripped up or focusing on the wrong things. 

Let’s take another example:

I interviewed this candidate for one of the roles in my team. I generally keep the first three to four minutes for mutual introductions and want to give the candidate as much time as possible for the technical part of the interview. As such, I let the candidate know that I am just looking for a 30 thousand foot view on their background. 

The candidate I was interviewing wanted to cover his entire background as an intro. This went on for over five minutes and got me worried that we might not get enough time for the technical case. As such, I tried to hint that we could move on. The candidate did not take the hint, and went on for another three to four minutes. By the time we started the case, we were 10 minutes in and, as suspected, we were not able to finish the interview on time. There were two challenges here: 

  • The candidate did not look for or acknowledge my cues.
  • The candidate spent time talking about experiences which were not relevant for the role of a data scientist. That extra time hurt them towards the end.

Use positive body language: Body language is such a critical part of the overall interview experience. For in-person interviews, demonstrating enthusiasm and positive energy can do wonders. And for some reason, if things feel less than perfect, let the interviewer or coordinator know. 

Let’s consider this example:

In one of my interviews (where I was the candidate), I was famished by the time the last interviewer walked in. I immediately told him I needed a couple of minutes to munch on a snack bar. This request was not a big deal, and he was totally fine with it. Asking for a small break didn't hurt my chances. In fact, if I were the interviewer, I would have appreciated that gesture as It shows me that the candidate is trying to be their best for the interview. 


Qualified data science candidates should realize that it's not enough to be technically brilliant: they need to be able to articulate their thinking and be aware of social cues to ensure better communication, especially in a limited interviewing setting. These tips should be helpful for those highly skilled candidates who still struggle in landing the job, and will be especially useful in business case-heavy interviews like the ones at DoorDash.  

As a parting note, remember that the interviewer and the interviewee are in this process together. The evaluation is happening in both directions so it's important that candidates hone their social skills to recognize when the interviewer is trying to help. After all, while the interviewer is looking for the right candidate for the role, the interviewee should be considering if the role is right for them.

Happy interviewing!!

If you are interested in joining an amazing Data Science team, we are actively hiring for Data Scientists and Senior Business Intelligence Engineers, as well as several other leadership roles on the team. If you are interested in working on other areas at DoorDash check out our careers page.

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