Lyft Data Science Interview Questions

As of January 2018, Lyft could count 23 million users.

Vimarsh Karbhari
Jun 11 · 3 min read

Lyft currently offers services in 350 US cities, and Toronto and Ottawa in Canada. It was launched in 2012, as a part of long-distance car-pooling business Zimride — the largest such app in the US (named for transportation culture in Zimbabwe). It was renamed as Lyft later. Launched in Silicon Valley, Lyft spread from 60 US cities in April 2014 to 300 in January 2017, to 350 today — plus the two aforementioned Canadian cities. With 350 cities, millions of users and billions of rides the data generated at Lyft is huge. The product achieves economies of scale deploying Data Science. Hence, data science is a core part of the product and not just an added feature.

Interview Process

The interview process starts with a phone interview with a Data Scientist. It is around an in depth conversation about your resume and past projects. That interview is followed by a take home test which is usually around a ride sharing data set. As part of the take home test, there is a presentation which has to be created for the onsite interview. The onsite interview consists of 4–5 interviews. One of those is presentation of the take home test. It also includes a SQL test, stats and probability and business case. There is a final core values interview to know if you fit within the Lyft culture. The interview is challenge but the reward when you clear the interview is totally worth it.

Important Reading

Data Science Related Interview Questions

  • Find expectations of a random variable with basic distribution. How would you construct a confidence interval? How would you estimate a probability of ordering a ride? What assumptions do you need in order to estimate this probability?
  • What optimization techniques are you familiar with and how do they work? How would you find the optimal price given a linear demand function?
  • Coin got x heads during y flips. How can we test if this is a fair coin?
  • What are some metrics for monitoring supply and demand in Lyft market?
  • Explain correlation and variance.
  • What is the lifetime value of a driver?
  • Implement k nearest neighbour using a quad tree.
  • What are the different factors that could influence a rise in average wait time of a driver?
  • Explain what are the best ways to achieve pool matching?
  • How do you reduce churn on the supply side?

Reflecting on the Questions

The Data Science team at Lyft moves very quickly. The Data sets are huge and problems so wide in nature that the team explores different types of models which can provide higher precision for same recall and feature set. The questions reflect the tough problems which the team faces day to day. There is a mix of model building along with complex coding questions. As I mentioned before the interviews are tough but they are well worth it for getting to work in an excellent team. Hard work can surely get you a job in one of the world’s largest transportation companies!

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The sole motivation of this blog article is to learn about Lyft and its technologies helping people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.

Acing AI

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Vimarsh Karbhari

Written by

Engineering Manager | Editor/Founder of Acing AI

Acing AI

Acing AI

Acing AI provides analysis of AI companies and ways to venture into them.