Quick summary
Summarize this blog with AI
Historical candidate account: this article preserves one person’s reported experience. The interview date was not recorded in the original text, and updating this page does not make the account a current hiring process. Names, levels, durations, questions, outcomes, and approximate compensation are as reported, not independently verified or endorsed by the employer. Confirm the current role and assessment with the recruiter.
Lyft data scientist interview case study from an instamentor student.
Overall it took 5 weeks.
TLDR
Candidate: N/A
How it gets started: Recruiter reached out on LinkedIn
Job level: T5
Year of Experience: 5–10
Degree: M.S & B.S. in CS
Offer: Yes
TC: ~450K USD
Location: San Francisco
Interview process: 5 weeks
Preparation: 2 months
Has a job: yes
Decide to join: N/A
Round 1: HR call
Why are you interested in Lyft, why do you wanna leave your current job, are you willing to relocate to San Francisco (in the city, where the HQ is based)
Round 2: Statistics & Probability & metrics
Fellow data scientist from the team gave a call and deep dive into the resume and focus on technical knowledge.
How do you choose the right metrics to evaluate the healthy of Lyft's carpool service?
How do we know if we are performing well/poorly in this newly expanded city?
Round 3: Take-home data challenge
Given the existing A/B testing data, how can we improve the cancellation policy? What is your conclusion?
3 datasets: control + treatment 1 + treatment 2
Asked to submit a keynote presentation
Round 4: virtual onsite/final round
SQL: window functions, rank, lag/lead
Leadership/Behavioral questions. Tell me about a time you take a lead and go far and beyond for your customer. How do you influence others without being the manager?
Product case study: how to evaluate customer experience, what metrics to use, how to improve.
A/B testing. What if p-value is > 5%
Presentation. Based on the submitted homework, what is your conclusion, what should we do, anything actionable based on the data?
Final Offer
The original account described an offer of roughly $450,000 and listed about $210,000 base pay, a roughly $900,000 four-year equity grant, and a $25,000 sign-on payment. Those components use different time bases, and the original figures do not reconcile to a single precise annual total. They are retained as reported historical details, not a current compensation benchmark or a verified valuation.
Prepare a cancellation-policy recommendation
A cancellation policy affects riders and drivers, so define outcomes for both sides: completed rides, wait times, cancellation behavior, and economic consequences under a stated measurement contract. For the reported control and two-treatment exercise, check randomization, assignment counts, follow-up windows, and the multiplicity plan before comparing effects. A p-value above 0.05 is not proof of no effect. Present the estimated differences, uncertainty, guardrails, and the decision your evidence can support.
Practice one reported prompt, state the data and assumptions, produce a checkable answer, and explain one limitation. Then change a condition and see whether your reasoning still holds. Use this related practice resource for a follow-up; it is separate from the historical interview report.
