Lyft/Uber Metric Interview Question and Answer: Tips for Data Science Interview Success!
TLDRIn this video, Emma revisits her channel to address the demand for insights on real interview scenarios, specifically tackling a Lyft metric interview question. She outlines a systematic approach to diagnosing a problem with an increased average ETA, engaging as both interviewer and interviewee to demonstrate effective communication and analytical strategies. The video aims to equip viewers with the skills to dissect such metric questions, emphasizing the importance of clarity, systematic investigation, and interaction with the interviewer.
Takeaways
- 📝 The video is a follow-up on a previous video about cracking metric interviews, focusing on a real interview question from Lyft.
- 🔍 The interview question involves investigating why the average ETA has increased by three minutes.
- 📈 The video outlines a six-step framework to approach metric diagnosis questions, emphasizing systematic investigation.
- 🤔 The importance of clarifying the scenario and the metric is highlighted to ensure a full understanding of the problem.
- 🕒 The script discusses investigating the issue from different perspectives, including time factors and potential outliers.
- 🔑 It is suggested to check for algorithm changes, data collection processes, and other factors that could affect ETA predictions.
- 📊 The video emphasizes the need to segment the metric by different dimensions to isolate the issue, such as region or platform.
- 📉 The approach includes comparing ETA against actual time of arrival to identify discrepancies and understand the root cause.
- 🗣️ Interaction with the interviewer is crucial for metric interviews, as it helps to confirm the approach and adjust based on feedback.
- 📝 The video provides an example of how to answer the Lyft interview question, demonstrating the thought process and investigation steps.
- 💡 The takeaway encourages viewers to share difficult interview questions for further guidance and potential inclusion in future videos.
Q & A
What is the main focus of the video by Emma?
-The main focus of the video is to discuss a real metric interview question from Lyft and to provide a step-by-step approach to answering such questions, including acting as both the interviewer and interviewee to demonstrate what a real interview might look like.
What is the specific interview question Emma discusses in the video?
-The specific interview question is about an increase in the average ETA (Estimated Time of Arrival) by three minutes on a dashboard and how one would investigate this problem.
What are the six steps Emma provides for diagnosing a metric problem?
-The six steps are: 1) Clarify the scenario and the metric, 2) Investigate the time factor to see if the change happened suddenly or progressively, 3) Check for outliers, 4) Segment the metric by user demographic and behavioral features, 5) Decompose the metric if possible, and 6) Summarize the overall approach to show a systematic method for solving the problem.
Why is it important to check for outliers when diagnosing a metric problem?
-Checking for outliers is important because extreme values can significantly impact the average metric, and understanding if the increase in average ETA is due to outliers can help in identifying if the data was collected correctly or if there were technical issues.
What does Emma suggest doing if the average ETA increased suddenly?
-If the average ETA increased suddenly, Emma suggests checking if there were any changes to the ETA calculation algorithm, the rider-matching algorithm, or the data collection process that might have caused the increase.
How should one proceed if the average ETA increase is happening progressively?
-If the increase is progressive, one should look at historical trends, compare ETA against actual time of arrival, and consider factors such as changes in the number of riders and drivers, which might indicate a supply and demand issue.
What is the importance of segmenting the metric by different dimensions?
-Segmenting the metric by different dimensions such as region and platform can help narrow down the issue to specific areas or systems, allowing for more targeted investigation and resolution.
Why is it recommended to interact with the interviewer during a metric interview?
-Interacting with the interviewer is recommended because it helps to confirm whether the approach makes sense, allows for clarification, and shows that the interviewee can explain their thought process robustly and adapt to feedback.
What does Emma suggest doing if none of the initial factors seem to be causing the increase in average ETA?
-If none of the initial factors are causing the increase, Emma suggests further segmenting the metric by dimensions such as region and platform to identify if the issue is specific to certain areas or systems.
How should one summarize their approach to diagnosing the issue in an interview?
-One should summarize their approach by outlining the steps taken to understand the data, identify factors affecting the metric, and analyze if the change is regional or platform-specific, ensuring the summary demonstrates a systematic and organized method.
What does Emma offer to do for viewers who have difficult interview questions?
-Emma offers to provide guidance on how to answer difficult interview questions by selecting a few and making videos to help viewers answer them better, as long as it does not violate any NDA.
Outlines
📊 Interview Question Analysis on Average ETA Increase
Emma introduces a video focused on real metric interview questions, specifically addressing a scenario from Lyft where the average ETA has increased by three minutes. She outlines a framework to diagnose such problems, emphasizing the importance of understanding the metric and investigating the issue from various angles. Emma provides a six-step approach to systematically tackle the problem, suggesting that interviewees adapt the steps based on the situation and feedback from the interviewer. The video aims to help viewers prepare for similar diagnostic questions in their interviews.
🔍 Investigating the Root Cause of Average ETA Changes
In this paragraph, the script delves into the specifics of diagnosing the increase in average ETA. The speaker, acting as both interviewer and interviewee, discusses the importance of checking for outliers and understanding the definition of the metric. They explore the possibility of sudden changes due to algorithm adjustments or data collection issues and consider progressive changes that might indicate larger systemic problems. The speaker suggests analyzing trends, comparing ETAs with actual arrival times, and assessing the impact of ridership and driver availability. The approach is to segment the metric by region and platform to pinpoint the cause of the increase in average ETA.
🗣️ Interview Dynamics and Approach Summary
The final paragraph of the script reflects on the interview dynamics and summarizes the approach to diagnosing the average ETA increase. It highlights the importance of not strictly adhering to a framework but rather adapting to the specifics of the question. The speaker emphasizes the need for interaction with the interviewer to ensure the approach is clear and robust. The video concludes with an invitation for viewers to share challenging interview questions for further guidance and a promise of future content to assist with interview preparation.
Mindmap
Keywords
💡Metric Interview
💡Dashboard
💡Average ETA
💡Diagnosis
💡Outliers
💡Algorithm
💡Time Aspects
💡Segmentation
💡Systematic Approach
💡Interaction
💡NDA
Highlights
Emma introduces a video on cracking metric interviews, focusing on a real question from Lyft.
The video aims to help viewers prepare for interviews by discussing a problem diagnosis type question regarding average ETA increase.
Emma provides a six-step framework to approach metric questions, emphasizing the need for a systematic method.
Clarification of the scenario and metric is the first step to ensure a full understanding of the problem.
Investigation of the issue from different perspectives includes looking at time factors and sudden or progressive changes.
Checking for outliers and their impact on the average value is crucial for accurate diagnosis.
Emma suggests examining if algorithm changes could be causing the increase in ETAs.
The importance of comparing ETA against actual time of arrival to identify discrepancies is discussed.
Segmenting the metric by user demographic and behavior can help isolate the issue.
Emma emphasizes the flexibility of the framework and the importance of adapting to the interviewer's feedback.
The role of interaction with the interviewer in metric interviews to ensure the approach is clear and robust.
Emma acts as both interviewer and interviewee to demonstrate how to answer the Lyft question in a real interview scenario.
The importance of summarizing the approach to show a systematic method for problem-solving.
Emma discusses the potential impact of regional factors and platform-specific changes on the average ETA.
The video concludes with a call to action for viewers to share difficult interview questions for further guidance.
Emma offers to select and answer viewer-submitted interview questions in future videos.
The video provides a comprehensive guide on how to tackle metric interview questions effectively.
Transcripts
Browse More Related Video
Crack Metric/Business Case/Product Sense Problems for Data Scientists | Data Science Interviews
BEST Answers to the 10 Most Asked Interview Questions | Interview Questions and Answers
Data Science Job Interview – Full Mock Interview
LinkedIn Metric Interview Question and Answer: Tips for Data Science Interview Success!
Example Cambridge Engineering Interview
5 Tips to OVERCOME Interview NERVES! (How to NOT be NERVOUS in a Job Interview!)
5.0 / 5 (0 votes)
Thanks for rating: