Recently accompanied a trainee through Lyft The whole interview process of Data Analyst is honestly more comprehensive than expected. In the first round, we talked about business logic, in the second round, we did take-home, and in the third round, we had to demonstrate + SQL + behavioral interviews, which were interlocked, and it felt like we were "pushing you through" in a real work scenario. At first, the students were quite panicked, especially because they were not familiar with the mobility industry, and they thought they would fail in answering the business case. However, after doing several simulations together, we gradually grasped the key points of Lyft's investigation: not only do you need to be able to run out the data, but you also need to be able to explain clearly what it means to the business. In the end, he successfully got the offer, which was a complete process to get to the bank.
Background of trainees
This student has two years of experience in data analysis in the e-commerce industry, and usually does a lot of SQL + Tableau, and has a good foundation in statistics. However, he is relatively new to the mobility business, so when he prepared for Lyft, he focused on cases related to the mobility industry.
The scene of the interview
Round 1: Business Case Questions
The biggest concern for trainees at the beginning is the business questions. For example, the interviewer asks, "What would you investigate if you found that the ride completion rate had dropped?"
He had ideas in his head, but he was worried about expressing them in an organized way. At this time, we gave him a few simulations in advance, and practiced the case framework until it was easy to understand: first break down the problem → put forward hypotheses → list indicators → verify the method. On the day of the interview, he answered according to the formula, and even took the initiative to add a sentence "we will cross-check the data with the ops team", which brightened up the interviewer's eyes.
Round Two: Take-home Challenge
Take-home got a real business data and asked to clean+visualize+write a report, the trainee had no problem to write SQL, but at the beginning, the charts were "stacked" and lacked narrative. The trainee had no problem writing SQL, but the charts he made at the beginning were quite "stacked" and lacked narrative, we reminded him to think from the business perspective, for example, when showing rider retention, instead of just putting a line graph, he should explain the impact of retention on revenue. We reminded him to think more from a business perspective. In the end, the logic of the report was clear, and the presentation went smoothly.
Round 3: Presentation + SQL + Behavioral
This round is the most difficult, both to show take-home and to write SQL on the spot. there is a cohort retention window function in the SQL question, the student almost missed the edge case, we reminded "pay attention to the date trunc" in the voice assistant in time to avoid the low-level error. We reminded the trainee in the voice assistant "pay attention to date trunc" in time to avoid the low-level error.
As for behavioral, we helped him put together 3 sets of STAR stories in advance, which he applied directly during the interview, and his answers were very complete. For example, when asked how he handles disagreements with the business side, he told a story about driving a new strategy at an e-commerce company, which demonstrated his communication skills and fit Lyft's values.
Lyft Data Analyst - FAQ
Q: Should Take-home use machine learning?
A: No, the focus is on data visualization and business insight.
Q: How difficult is SQL?
A: Medium, note the edge cases.
Q: What does Behavioral ask?
A: The common ones are conflict management and cross-team communication. It is recommended to review several real-life examples using the STAR method.
Q：How to prepare for Business case?
A: Research Lyft's financial results, business model, and core industry metrics (DAU, retention, utilization).
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