Congratulations. Your simple, beautiful, elegant resume has sailed you through to Round 2. Perhaps you passed a technical assessment or spoke to a recruiter, but in any case, now the real fun begins: a call with someone on the Data Science team.
Most likely you’ll be chatting with the role’s hiring manager, but the screen could also be with a senior IC (individual contributor).
Preparation
As always, success begins ahead of time.
Read up on the company to which you’ve applied. Sounds simple, yet so many candidates don’t bother. Read their website, read their core values, read their tech blog, read anything you can find. Use this knowledge to conceive of questions that will set you apart from the crowd.
Some places will prep you on what you’ll be asked: probability, statistics, programming, case studies. But in general, go in expecting anything. Never get caught out because you were quizzed on something for which you weren’t forewarned.
Introduction
Be ready by your phone or laptop a few minutes prior to the call. If it’s a Zoom, however, do not join too early - the interviewer will get an email informing them you are in the room. Instead, just be on time. There’s nothing worse than a candidate hastily pulling their laptop into view right as the interview begins.
Dress…normal. You don’t need a suit, but don’t be in your PJs. Look presentable.
On Zoom, your background should indicate professionalism. Make it well-lit. Sit at a desk, with books, a plant, or something nicer than a bare wall reminiscent of a prison cell behind you. If you don’t have a good spot, then use an appropriate digital background. Test your mic and audio set-up ahead of time.
When the call begins, be cheerful, but not manic. You don’t have to pretend this is the greatest day of your life, but you shouldn’t be facing a death sentence, either.
After some pleasantries, the interviewer will likely ask you to introduce yourself. Make sure to not regurgitate your resume. They’ve already read it. Tell your story instead. In 30 seconds, pick out the aspects of your background that make this role a logical next step. The interviewer should see a progression which explains to them why you are interested in this role, and more importantly, why you are such a great fit.
Questions
We now enter the main event. Deep breath.
Have a pen and paper. Take notes. Case studies in particular can be a bit lengthy, and you don’t want to miss anything. Start thinking of ideas as you write, but don’t panic if nothing comes to you immediately - sometimes an answer won’t be apparent until the full question is read.
For each problem, limit yourself to one clarifying question. After that the interviewer will think you are time wasting. And yes, if you have no idea, ask a clarifying question to buy some time.
As in radio, avoid dead air. Take maybe 4 or 5 seconds to collect your thoughts, but avoid long periods of silence. It’s good (and expected!) to walk an interviewer through your thought process. Most interviewers are simply looking to assess how you approach a problem. Understanding if you’re a clear thinker is often as important as whether you can jump straight to the right answer.
Don’t give up on a question. Keep generating ideas: a good interviewer will usually provide hints, and as you are responding check for context clues to indicate when you’re heading in the right direction.
You will rarely need to ace everything to make it through to the next round. Just do the best you can.
Wrap-Up
Once the technical component is over, the interviewer will ask some variation on “What questions do you have for me?” Think back to your earlier preparation - now is the time to put it to use.
Ask 2 or 3 questions total. A good question has specificity. Ease the cognitive burden on the interviewer. Do not ask something so open-ended as to be boundless: “What’s it like to be a Data Scientist at Company X?” Instead, ask about a specific aspect of the Data Science team: its structure, its culture, its prioritization philosophy, its tech stack. Give the interviewer some direction.
Having said that, don’t be trivial. Inquiring about what’s served for lunch on Wednesday is not a good idea. Think of questions that connect the Data Science team to the company’s larger mission and strategy (and, in return, help you understand how the team is truly perceived within the organization).
No questions about compensation. This is not the moment, and in any case, it’s much easier to talk about salary and benefits once you have an offer.
As the interview ends, thank the interviewer for their time, and gracefully say goodbye. A follow-up note is nice but not expected.
Regardless of how the screen went, enjoy the rest of your day.
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