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      Lyft

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      Data Scientist Interview

      21 Mar 2020
      Anonymous interview candidate
      New York, NY

      Other Data Scientist interview reviews for Lyft

      Data Scientist Interview

      4 May 2026
      Anonymous interview candidate
      No offer
      Negative experience
      Easy interview

      Application

      No offer
      Negative experience
      Easy interview

      Application

      I applied online. The process took 4 weeks. I interviewed at Lyft (New York, NY) in Mar 2020

      Interview

      Applied online and was contacted by a Lyft recruiter. Phone screen was very basic (20 minutes, motivation, why Lyft etc) and I was quickly scheduled for a second round interview with one of their Data Scientists. Again, interview was a breeze (30 minutes, describe past projects and a quick "business case" where you basically just think aloud about a problem and relevant KPIs). Was again quickly moved along to the take home assignment, which as many people here have noted isn't complex per se but is quite a bit longer than advertised if you want to do it right. I sent them a full powerpoint with presenter notes and 2 Jupyter notebooks for data cleaning and analysis. Received a 2 line rejection email 3 days after handing in my assignment telling me I wasn't technical enough. When I asked for more feedback it turns out what they meant is I didn't comment my code enough and I didn't expand on one aspect they thought was important; so nothing technical but pure domain knowledge actually. To me, a take home assignment is for the company to evaluate technical competency and critical thinking and not so much about having the "right" answer, but I guess not for Lyft. My advice to candidates: steer clear, don't waste your time. My advice to Lyft: use the assignment in a smarter way, or if domain knowledge is an actual prerequisite, make that clear and gear the interviews ahead of the assignment that way, because getting through interviews is a walk in the park and then you expect something very specific from the assignment which isn't very fair to candidates who spend hours working on it.

      Interview questions [1]

      Question 1

      What indicators would you use to evaluate whether a city is running at over/under/normal capacity?
      Answer question
      9

      I interviewed at Lyft

      Interview

      I interviewed with a recruiter last fall. Since I was graduating in eight months, she said she'd talk to the hiring manager about whether to send technical interviews now or hold off for different openings in the future, and would get back to me in a few days. She never followed up and didn't respond to my emails. Eventually, I got an automated rejection, the subject line literally read "Update on [insert job title]," they hadn't even bothered to fill in the job title. The whole process felt unprofessional.

      Data Scientist Interview

      22 Aug 2025
      Anonymous employee
      Toronto, ON
      Accepted offer
      Positive experience
      Average interview

      Application

      I interviewed at Lyft (Toronto, ON)

      Interview

      Case Interview -> Final Interview Loop (ML Case, Business Case, Coding, Behavioural) Recruiter reached out. Interviews got scheduled. Interviews happened. Offer got made. Negotiation happened for few days. Offer got accepted.

      Interview questions [1]

      Question 1

      Case Interview -> Final Interview Loop (ML Case, Business Case, Coding, Behavioural)
      Answer question

      Data Scientist Interview

      16 Sept 2025
      Anonymous interview candidate
      Toronto, ON
      No offer
      Negative experience
      Average interview

      Application

      I interviewed at Lyft (Toronto, ON) in Aug 2025

      Interview

      “I completed all interview rounds — including HR, technical screening, product sense, business case, algorithm live coding, decisions live coding, machine learning, and experience interviews — but the company ultimately selected another candidate

      Interview questions [1]

      Question 1

      Have a good understanding of stats/probabilities/ML/Coding(SQL,Python), and business domain and metrics.
      Answer question