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Lyft Data Scientist Interview Questions

Interviews at Lyft

21 Interview Reviews

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Helpful (5)  

Data Scientist Interview

Anonymous Interview Candidate in San Francisco, CA
No Offer
Neutral Experience
Difficult Interview

Application

I applied through an employee referral. The process took 1 day. I interviewed at Lyft (San Francisco, CA) in June 2016.

Interview

Process was relatively long. I was recommended by an engineer there who also introduced me to the Hiring Manager. After meeting I had a take-home challenge, followed by two technical phone screens. I finally got invited onsite. Onsite interviews were really tough, focused on probabilistic modeling.

Interview Questions

  • Describe how to engineer the heatmap telling drivers where to go. How do you define which area will have high demand next and who do you want to go there?   1 Answer
  • How do you model the impact of surge on demand and supply?   Answer Question

Other Interview Reviews for Lyft

  1. Helpful (2)  

    Data Scientist Interview

    Anonymous Interview Candidate
    No Offer
    Positive Experience
    Difficult Interview

    Application

    I applied through a recruiter. The process took 6 weeks. I interviewed at Lyft in January 2017.

    Interview

    I applied in a conference. Then they sent me a take-home challenge. 6 hrs time limit. The challenge was very interesting, about transportation optimization. I have used python to code my optimization algorithm. The also required a writeup report about your assumptions, limitations, and conclusions. I was rejected later in less than a week.

    Interview Questions


  2. Helpful (1)  

    Data Scientist Interview

    Anonymous Interview Candidate in San Francisco, CA
    No Offer
    Neutral Experience
    Average Interview

    Application

    I applied through other source. I interviewed at Lyft (San Francisco, CA) in November 2016.

    Interview

    I saw them in a conference and I got a phone interview first. After phone interview they sent me a data challenge project about car sharing. Unfortunately, they did not gave me onsite interview after I submitted the challenge.

    Interview Questions

    • Some basic statistics e.g. correlation and variance.   1 Answer
  3. Helpful (15)  

    Data Scientist Interview

    Anonymous Interview Candidate in San Francisco, CA
    No Offer
    Negative Experience

    Application

    I applied through an employee referral. I interviewed at Lyft (San Francisco, CA) in August 2017.

    Interview

    The interview process I had was:
    1. Take home test (24 hour time limit)
    2. Two technical 45 min phone interviews
    3. Onsite with five 1:1 technical interviews (all whiteboarding)

    The team took way too long to get back to me after I completed the onsite. I had waited almost 2 weeks without hearing anything from them. I only heard back after I chased the recruiter on it -- only to receive an extremely curt response that the team was passing on me. When I requested feedback, the recruiter said that they don't like giving feedback to candidates. This really suggests that the company doesn't prioritize transparency and accountability in their hiring (e.g. can they strengthen and standardize their evaluations enough to feel good about releasing them?) and that they don't value the candidates' time and effort (e.g. what is the candidate getting out of this insanely long and arduous process if they're not learning where they can improve?).

    Additionally, this was the most narrow-minded data science interview process I've ever heard of. This is the first time I've seen a company give *only* technical screens throughout the entire interview process. I get that it's a technical role, but it leaves a bad impression when you're evaluating candidates on only one dimension of their abilities, when data science is necessarily a very multi-dimensional role. Contrary to how other companies do it, they also didn't seem to want to bother digging into my past experience and project work (which I would have been happy to be tested on), and instead wanted to continuously test me only on the same two or three concepts that are apparently emphasized in their own data science team.

    Aside from the structure of the interview process: most of the data scientists who interviewed me were very nice and accommodating. They kept the interviews and case problems somewhat interesting, and, in turn, gave thoughtful answers to my questions. Save for one of them who was pretty rudely dismissive to me when I asked them whether their team is interested in X field of data science.

    Interview Questions

    • Multiple graph optimization q's   3 Answers

  4. Helpful (5)  

    Data Scientist Interview

    Anonymous Interview Candidate in San Francisco, CA
    No Offer
    Negative Experience
    Easy Interview

    Application

    I applied through a recruiter. I interviewed at Lyft (San Francisco, CA) in June 2017.

    Interview

    Phone screen followed by a take home exam. Phone screen was friendly. Take home was very poorly specified. I answered all of the questions asked very directly and solidly but failed the interview because they were looking for far more detail. It's the most screwy interview loop I've done in Silicon Valley.

    Interview Questions


  5. Helpful (4)  

    Data Scientist Interview

    Anonymous Interview Candidate in Palo Alto, CA
    No Offer
    Negative Experience
    Difficult Interview

    Application

    I applied online. The process took 3 weeks. I interviewed at Lyft (Palo Alto, CA) in July 2017.

    Interview

    There was an initial technical phone screen involving a coding challenge followed by a second technical phone screen involving another coding challenge. I was able to complete the first challenge, and expected that the second one would pertain more to the role description than it did. I wasn't really asked any questions related to the skills I that I have in my background that were relevant to the role described in the job posting.

    Interview Questions

    • The recruiter had said that the interview challenge revolve around api development. Instead, they asked me to implement k nearest neighbor using a quad tree.   2 Answers

  6. Helpful (18)  

    Data Scientist Interview

    Anonymous Employee in San Francisco, CA
    Accepted Offer
    Positive Experience
    Difficult Interview

    Application

    I applied through an employee referral. I interviewed at Lyft (San Francisco, CA) in July 2017.

    Interview

    One of my friends referred to me. I got formally contacted a week later.

    1. Exploratory discussion with recruiter (~30min)
    2. Data Challenge to complete at home (I took 6 hours to complete it; there is a 24 hours time limit).
    3. Technical phone interview (30~45min) on probability and optimization. Focus is on fundamentals, there is no trick.
    4. On-site interview: 5 or 6 technical interviews on various topics. Each of them lasted 30~45min.

    Overall, it was a nice and smooth process. Lyft accommodated me with my other deadlines and calendar constraints and moved very fast.


  7. Helpful (3)  

    Data Scientist Interview

    Anonymous Interview Candidate in San Francisco, CA
    No Offer
    Neutral Experience
    Average Interview

    Application

    I applied online. The process took 1+ week. I interviewed at Lyft (San Francisco, CA) in June 2018.

    Interview

    Interview process was good. HR was quick and responsive. Went through a phone screening, followed by a technical interview and homework assignment.

    Phone screening was pretty basic and a run down of my resume.
    In the technical round, the interviewer was friendly and polite. Was asked how I would approach finding a solution for a driver-based problem.

    Homework assignment was pretty straight forward.

    Interview Questions

    • What are the different factors that could influence a rise in average wait time of a driver.   1 Answer
  8. Helpful (8)  

    Data Scientist Interview

    Anonymous Interview Candidate in San Francisco, CA
    No Offer
    Positive Experience
    Average Interview

    Application

    I applied online. The process took 3 weeks. I interviewed at Lyft (San Francisco, CA) in July 2018.

    Interview

    Got a call back from the recruiter after online resume application. Initial phone screen with HR regarding the role, team and your background fit. One 30-45 min phone screen with data scientist which is mostly about a business case study and your technical experience and projects. Next was a 48 hours data challenge, with multiple questions and subquestions. it has a good balance between the technical and business side of the problem. This is probably the hardest part of the interview process as you have to build a coherent story with your analysis while answering all the questions asked. Followed by half a day of on-site interviews data-challenge presentation, 2 business case studies, stats and probability, technical (SQL and R/python) and values and cultural fit interviews with varied team members with different experience level. Overall the onsite experience was pleasant, but I struggled in one of the business cases as the question was described in a line as opposed to a business situation in other interviews at Lyft or other firms. This either meant I had to ask multiple questions or make numerous assumptions. Also, the way the interviewer wanted to approach the problem was radically different to what I proposed and it just didn't go well with the interviewer. I must point out that he was extremely polite and not at all condescending throughout the 30 mins. Stats and technical interviews were pretty easy if you know business applications of key concepts, their variants and data manipulation using SQL(window functions). Overall a great bunch of people to talk with, was given quick results and accommodated my schedule. The recruiter also called to give feedback after conveying the decision. I still feel more business context should be given during business case studies. All the questions are related to Lyft's business model and their product

    Interview Questions

    • Window functions, Stats of A/B testing, Probability and expectation, Churn, Lifetime of drivers, Pool matching   4 Answers

  9. Helpful (6)  

    Data Scientist Interview

    Anonymous Interview Candidate
    No Offer
    Positive Experience
    Difficult Interview

    Interview

    Overall straightforward interview -- but, make sure to know your ride-sharing metrics and how experiments look like at a ride-sharing company or else you are screwed. Interview process took 1 month.

    FYI, in the Lyft context "Data scientist" = product analyst, for the most part

    Process:

    (1) Got referral to start process
    (2) Phone screen w/ current data scientist. Go in depth with a past project, talk through the math of hypothesis testing ("why use a t-distribution in this scenario?"), talk through success metrics for a ride-sharing business case
    (3) Take home: there are a set of questions you have to answer with a simple ride-sharing dataset (how to measure churn, set up experiment for recommendation to reduce churn) create presentation for on-site.
    (4) On-site: 5 interviews. (1) Presentation of take home. My advice is to evaluate your definition of churn with a false positive rate and know about clever experiment set-up (i.e. splitting your experiment across multiple cities, etc.) (2) SQL test (3) business case -- be sure to know the unit economics metrics related to ride-sharing. Be able to answer questions like "what data told us to create Lyft shared rides?" (answer: overlapping routes in map data) (4) stats & probability: had to answer the hypothesis testing classic "Coin got x heads during y flips. How can we test if this is a fair coin" and then pivoted to an ride-sharing experimentation question (5) Core values interview: really cool to hear about how Lyft thinks about success, culture, and evaluating the performance of data science ICs

    I didn't get an offer. Basically, if I were do this again I would do the metric prep that I did and then ALSO grab coffee with a friend working in ride-sharing data science to make sure I understand the metrics that matter.


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