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Superhuman Data Scientist Interview Questions
& Process

Real candidates share what happened, how many rounds they had,
and how the experience turned out.

Based on 20 interview experiences · FREE TO READ

4.7 Rounds average
Average Typical difficulty
75% Positive experience

Candidate interview experiences

First-hand accounts from people who interviewed at Superhuman.

Showing 3 of 20
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Superhuman

Data Scientist

Analytics · more than a year ago

Senior Difficult Positive experience Accept offer 12 rounds
Interview process
Recruiter call Technical screen Technical screen Technical screen Technical screen Technical screen Technical screen Technical screen Technical screen Technical screen Technical screen Technical screen
Interview formats
Behavioral Technical

It was really well run! Steps: 1. Recruiter screen: was very relaxed and just talking about work background and what I was interested in. 2. Technical screen #1: Recruiter gave great materials on what to expect for this & ideas on how to prepare 3. Technical screens #2 & #3: Another round of technical screens that went well. It didn't feel like people were trying to trick me. 4. Technical Screen #4, Hiring manger interview, & x-functional: Again, recruiter gave great materials to prep, and it felt like the team wanted me to bring my best self, not trick me.

Confirmed questions2 questions
  • Can you interpret these graphs?
  • How would you interpret the results of this experiment?
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Superhuman

Data Scientist

Analytics

Entry Easy Negative experience No offer 3 rounds
Interview process
Recruiter call Take home Background check
Interview formats
Technical

I first had a 30-minute chat with the manager, then I received a take-home test that I had to complete within a week. After submitting it, I got rejected about a week later without any feedback. I really dislike take-home tests because they don't respect candidates' time. This one had 4 questions that required a lot of analysis in a Python notebook. The questions were intentionally open-ended to make candidates spend more time polishing their answers and considering alternatives, which I regret. A major issue is the unfair time investment - candidates spend hours on it, while the company might spend only 10 minutes reviewing. It's not like a typical interview where both sides invest similar time to assess mutual fit. In the future, if I'm asked to do a take-home test, I'll withdraw my application as it's not worth the time and there are better uses for it.

Confirmed questions1 question
  • Conduct an analysis of the 'ping' data and provide some recommendations based on your findings.
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Superhuman

Data Scientist

Analytics

Mid Average Positive experience Accept offer 7 rounds
Interview process
Recruiter call Technical screen Onsite Background check Offer
Interview formats
Behavioral Technical Case Presentation

It was a 1 hour interview to talk about my past work, find out about the job and team, and answer some product case questions. Then a 45 minute interview with SQL questions and product/business case questions. After that, a 1 hr interview to talk about my strengths and areas for improvement throughout my career. Next up was a 1 hr SQL and case study. Then a 1 hr a/b testing case study. Followed by a 45 minute product knowledge interview with a PM. Lastly, a 30 minute interview to talk about what I've liked and disliked in my career. The parts about my professional values and progression were really in-depth and made me think deeply. It was unlike other interview processes I've had, and it really showed how much Grammarly cares about its values. The whole thing went super smoothly. They sent me a really useful interview guide about what to expect and how to get ready for the virtual on-site. The recruiter kept in touch regularly to help coordinate with my other interviews. I really liked talking with everyone and appreciated the focus on analytical thinking and communication.

Confirmed questions1 question
  • How would you design and analyze an experiment to determine if a new product feature met its business objective?

Superhuman Data Scientist Interview Questions

Quoted word for word from Superhuman interview reports.

How would you design and analyze an experiment to determine if a new product feature met its business objective?

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Tell me about a Standard Data Science Interview in Grammarly's business context.

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What key learnings did you take away from your past experiences?

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Tell me about a project you're really proud of and how it helped the business.

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Formats, difficulty and experience

Across all 20 Superhuman interview reports.

Interview formats

Technical 34%
Behavioral 30%
Coding 12%
Case 12%
Presentation 10%

Interview difficulty

Easy 10%
Average 65%
Difficult 25%

Candidate experience

Negative 25%
Positive 75%