So, you’ve navigated the interview process and landed a job as a data scientist — great! And the company is successful and your team is growing — even better! But you’re suddenly tasked with conducting interviews for your new team members. Now what?

Across all programs and locations, Insight has helped more than 750 scientists and engineers transition to industry roles on top data teams. As the members of our alumni community advance in their careers, many find themselves moving into leadership and management positions that include interviewing and evaluating potential additions to the team. Earlier this year, we hosted panel discussions on best practices for interviewing data science candidates in both NYC and Silicon Valley.

Below, I highlight seven strategies for successful interviewing shared across these two panels.

Top: Insight alumni panelists from our Silicon Valley event: Sebastien de Larquier, Burcu Baran, Nhung Ho, Michael Woods. Bottom: Insight alumni panelists from our NYC event: Iva Vukicevic, Ethan Rosenthal, Noga Neeman. Special guest: Jonathan Roberts.

1. Leverage the knowledge of your existing team.

Michael Woods (Insight Fellow 2014, Search Quality Software Engineer @ Yelp) recommends using a two-person team for interview training — one will be the principal interviewer while the other acts as a shadow. As Michael states, “I’ve been shadowed and have shadowed, both of which help improve your interviewing aptitude.” To put your candidate at ease, introduce your shadow by name, noting that they’ll be there only as a passive observer, and then begin the normal interview process.

recommends using a two-person team for interview training — one will be the principal interviewer while the other acts as a shadow. As Michael states, “I’ve been shadowed and have shadowed, both of which help improve your interviewing aptitude.” To put your candidate at ease, introduce your shadow by name, noting that they’ll be there only as a passive observer, and then begin the normal interview process. Train each member of your team to specialize in a certain type of interview. Nhung Ho (Insight Fellow 2014, Data Science Manager @ Intuit) recommends that team members with the most experience and skill in an area can potentially help a candidate make the most progress on a technical problem, providing the most material for you to evaluate. So, for example, the team member strongest in statistics should be your go-to statistics interviewer.

2. Develop interview questions that accurately measure the skills most important for the role.

Good questions have many layers to them, allowing interviewers to turn up the difficulty and assess how quickly a candidate can learn. A multi-layered question also allows interviewees to take their responses in a number of different directions. Sebastien de Larquier (Insight Fellow 2013, Data Scientist @ Netflix) suggests that you “follow the candidate, go where they are going” so that each interview will be a little different.

suggests that you “follow the candidate, go where they are going” so that each interview will be a little different. Test new technical questions on your existing team members to establish a reasonable amount of time for candidates to complete them. Don’t forget to include the time needed to set up the problem.

While there may be specific brain teasers that are relevant to the problem-solving that happens on your team, they often aren’t a useful evaluation of a candidate’s skills and experience. Instead, Jonathan Roberts (Senior VP of Data Science & Audience Development @ About.com) advises that you ask open-ended questions framed in the world of the company’s problems.

advises that you ask open-ended questions framed in the world of the company’s problems. Don’t reuse the same questions in perpetuity. Create new questions for new roles and to evaluate candidates with unique backgrounds.

3. Be smart about the way you use data challenges.

A well-scoped data challenge with clearly defined goals can be used as a minimum threshold to get through to the next interview stage. But because they are used to test specific skills, challenges may not be that reusable from role to role.

As Nhung Ho warns, “don’t shoot yourself in the foot — experienced candidates won’t want to do a 4-hour take-home exam.” Reserve lengthy or complex data challenges for applicants with less experience. Senior candidates can provide sample code or side projects to evaluate.

warns, “don’t shoot yourself in the foot — experienced candidates won’t want to do a 4-hour take-home exam.” Reserve lengthy or complex data challenges for applicants with less experience. Senior candidates can provide sample code or side projects to evaluate. Balance the information you hope to gain from a data challenge with the overhead needed from your team to create and evaluate it. You should plan that it will take the same number of hours to prepare the challenge as for the candidate to finish it.

Jonathan Roberts shared his process for creating a data challenge: “Build a fairly messy dataset, intentionally. When candidates present the results of the challenge, ask them: How did you clean the data? What interesting things did you find? If you asked them to provide code, does it run? Did they exhibit reasonable skepticism about the data (for example, did they look for and appropriately handle outliers)?”

4. Interviews should feel like conversations, but use your time — and the candidate’s time — wisely.

Michael Woods notes that “managing your time well ensures that you cover all of your interview questions and makes it a smoother experience for the candidate.” Lay out a plan for the interview: five minutes for introductions, ten minutes for Question 1, five minutes for Question 2, etc.

notes that “managing your time well ensures that you cover all of your interview questions and makes it a smoother experience for the candidate.” Lay out a plan for the interview: five minutes for introductions, ten minutes for Question 1, five minutes for Question 2, etc. The interview should feel like a dialogue, so don’t necessarily leave the candidate’s questions until the end. Their questions provide insight into their priorities, motivations, and interests, so give them the opportunity to ask — and get answers — before time runs out.

If possible, arrange for all candidates for a specific role to be interviewed by the same group of team members. Sebastien de Larquier notes that most hiring managers at Netflix try to keep the same panel for the same role.

5. If the candidate gets stuck on a technical question, provide alternate ways to think about the problem.

Ethan Rosenthal (Insight Fellow 2015, Data Scientist @ Dia&Co) points out that you should help them in a way such that “you’re not giving them the answer, but you’re also not making them feel stupid.”

points out that you should help them in a way such that “you’re not giving them the answer, but you’re also not making them feel stupid.” Have the candidate explain their assumptions: Why are they approaching the problem in this way? In explaining their approach, they may realize their error. Everybody makes mistakes, but good candidates — and strong team members — notice and correct them.

Jonathan Roberts suggests that “the kindest way to help a candidate make progress is to restate the question and what you’re looking for in an answer.” This also has the benefit of making sure the interviewer has asked the question correctly.

suggests that “the kindest way to help a candidate make progress is to restate the question and what you’re looking for in an answer.” This also has the benefit of making sure the interviewer has asked the question correctly. Burcu Baran (Insight Fellow 2014, Senior Data Scientist @ LinkedIn) concludes that if you can’t get the candidate unstuck, perhaps that question isn’t a good way to evaluate them. “Move on to another question so you can give them the best chance at demonstrating their skills.”

6. Technical abilities are vital, but don’t neglect candidates’ communication skills.

To start the interview — and to evaluate the ability to communicate complex ideas — Noga Neeman (Insight Fellow 2015, Data Scientist @ Via) asks candidates to walk her through an analytics project they’ve worked on. As she notes, “even if what they’ve done is completely different, they should still be able to explain it if they have good communication skills.”

asks candidates to walk her through an analytics project they’ve worked on. As she notes, “even if what they’ve done is completely different, they should still be able to explain it if they have good communication skills.” Ethan Rosenthal adds that “everything is math, statistics, or programming in the end. A good candidate should be able to translate their research into common concepts that both can understand.”

adds that “everything is math, statistics, or programming in the end. A good candidate should be able to translate their research into common concepts that both can understand.” If you’re tasked with interviewing a potential team member who will carry out a parallel function, like a data engineer, you can still ask about a project they’ve worked on. As a data scientist, you wouldn’t be evaluating their technical skills but rather the way they approach — and explain — a problem.

7. Understand the evaluation plan before you begin the first interview.

Give yourself plenty of time to meet with and evaluate candidates so you aren’t tempted to rush or settle. The team needs to feel confident with the decision to move forward with someone (or not).

To reduce bias, some teams ask interviewers to not discuss their impressions of the candidate until all interviews have been conducted. Other teams prefer to share notes throughout the process, to give subsequent interviewers a chance to clarify outstanding questions about the candidate.

For a more systematic evaluation, ask interviewers to fill in a scorecard that rates each candidate using letter grades or a numerical scale for areas like technical ability, communication skills, cultural fit, etc. Leave room for open-ended comments, too.

Iva Vukicevic (Insight Fellow 2014, Data Scientist @ Macy’s) advises that if a candidate doesn’t meet your expectations for logic and communication, it’s unlikely these traits will improve on the job. However, if they show strong logic and communication skills but have some technical weaknesses, “consider investing in them — a smart worker who meshes well with your existing team can quickly be brought up to speed.”

We’re grateful to all of our panelists for taking the time to share their best practices for interviewing data science candidates. As you advance in your data science career, we hope these practical tips are helpful in growing cutting-edge companies and building high-caliber teams.

Becoming an expert interviewer adds tremendous value to your team and gives you a voice in who is — and isn’t — brought on board. Developing these skills can also aid in your own professional development, providing perspective as you navigate the many possibilities of a career in data science.