LinkedIn Metric Interview Question and Answer: Tips for Data Science Interview Success!

Emma Ding
6 Jan 202107:48
EducationalLearning
32 Likes 10 Comments

TLDRIn this video, Emma addresses a real metric interview question from the book 'Decode and Conquer,' which is beneficial for both product management and data science interviews. She discusses LinkedIn's feature of asking new users to upload a profile photo during sign-up and the importance of clarifying the feature's purpose. Emma suggests three key metrics for evaluating the feature's success: user engagement, the percentage of new users uploading photos, and a guardrail metric to ensure no negative impact on the sign-up process. She emphasizes the significance of choosing metrics that are easy to measure and fit the context, providing a sample answer to guide viewers through the thought process.

Takeaways
  • 📚 Emma introduces a real metric interview question from the book 'Decode and Conquer', which is useful for both product management and data science interviews.
  • 🔍 The original question is about evaluating the success of a new LinkedIn feature that asks users to upload a profile photo during the sign-up phase.
  • 🤔 Emma emphasizes the importance of understanding the feature's function and the business goal, which in this case is to improve user engagement and possibly detect fraud.
  • 📈 She suggests considering three types of metrics: success metrics, a guardrail metric, and interaction with the interviewer to provide a comprehensive answer.
  • 🎯 The first success metric is user engagement, measured by the average number of invites sent per user per day or the number of posts made.
  • 📷 The second success metric is the percentage of new users who upload their profile photos, which should be higher than the control group to validate the feature's usefulness.
  • 🚧 The guardrail metric is to monitor the percentage of users who drop off during the sign-up process, ensuring it does not increase due to the new feature.
  • 🔢 Emma discusses the importance of seeing real numbers and p-values to make data-informed decisions, especially when there are trade-offs between metrics.
  • 💡 She advises that if the feature increases engagement significantly but decreases the number of users completing sign-up, it might still be worth launching due to the primary goal.
  • 📝 Emma shares tips on choosing metrics: they should be easy to measure, fit the context, and not just follow a generic framework like AARR.
  • 🤝 Interaction with the interviewer is crucial, as it shows the candidate's ability to clarify questions and think on their feet.
  • 👋 Emma invites feedback on her approach to answering the interview question and encourages viewers to share their thoughts.
Q & A

  • What is the main purpose of the video?

    -The main purpose of the video is to discuss a real metric interview question from the book 'Decode and Conquer' and to provide a structured approach to answer such questions, particularly in the context of a data science interview.

  • Why is the book 'Decode and Conquer' mentioned in the video?

    -The book 'Decode and Conquer' is mentioned because it is a resource about product management interviews, but the speaker found it helpful for data science interviews as well, especially for metric-based questions.

  • What is the original interview question discussed in the video?

    -The original interview question is about evaluating the success of a new feature on LinkedIn that asks new users to upload their profile photo during the sign-up phase instead of after.

  • Why is the question considered typical for a data science interview?

    -The question is considered typical for a data science interview because it involves evaluating the success of a feature using metrics, which is a core competence of data scientists, including designing experiments, analyzing results, and making data-informed decisions.

  • What is the issue with the simple answer provided by the book according to the speaker?

    -The issue with the simple answer provided by the book is that the candidate does not clarify the function of the feature and comes up with many metrics that are not very feasible in A/B tests.

  • What are the two success metrics suggested by the speaker to evaluate the new feature?

    -The two success metrics suggested by the speaker are the average number of invites sent per user per day (to measure user engagement) and the percentage of new users who upload their profile photos (to measure the usefulness of the feature).

  • What is the 'guardrail metric' mentioned in the video and why is it important?

    -The 'guardrail metric' is the percentage of users who drop off in the sign-up process. It is important to ensure that in the pursuit of the new feature, the overall user experience does not degrade, and the sign-up completion rate does not decrease.

  • What is the goal of the new feature according to the speaker's interpretation?

    -The goal of the new feature, as interpreted by the speaker, is to improve user engagement by having more users upload their photos during the sign-up process, which may also help in detecting fraudulent accounts.

  • How does the speaker suggest interacting with the interviewer during the interview?

    -The speaker suggests clarifying the question, coming up with relevant metrics, and interacting with the interviewer to ensure a mutual understanding of the business goal and the potential impact of the new feature.

  • What is the speaker's advice on choosing metrics for an A/B test in an interview?

    -The speaker advises that for A/B tests, providing three metrics is usually sufficient, and the chosen metrics should be easy to measure and fit the context of the specific use case to avoid sounding robotic.

  • What is the final recommendation made by the speaker based on the hypothetical test results?

    -The final recommendation made by the speaker is to launch the feature, as having more engaged users, even with a slight decrease in sign-up completion, aligns with the current objective of improving engagement.

Outlines
00:00
📘 Introduction to Data Science Interview Question

Emma introduces a video focusing on a real metric interview question from the book 'Decode and Conquer', which is beneficial for both product management and data science interviews. She addresses the need for a more practical approach than theoretical frameworks, especially for evaluating the success of a product feature. The specific question involves LinkedIn testing a new feature that prompts users to upload a profile photo during the sign-up phase. Emma aims to provide a detailed answer that reflects a data science interview scenario, emphasizing the importance of clarifying the feature's function and setting measurable goals.

05:02
📊 Analyzing Metrics for Feature Success

In this paragraph, Emma discusses the approach to evaluating the success of a new feature that requires users to upload a profile photo during the sign-up process. She emphasizes the importance of understanding the feature's purpose, which is to improve user engagement by ensuring more users complete their profiles. Emma suggests two success metrics: user engagement, measured by the average number of invites or posts per user per day, and the percentage of new users uploading photos, which should be higher than the control group. Additionally, she proposes a guardrail metric to monitor the sign-up drop-off rate, ensuring it does not increase due to the new feature. Emma also addresses the importance of context in choosing metrics and the need for them to be measurable and relevant to the specific use case.

🔍 Evaluating Test Results and Decision Making

Emma explores a hypothetical scenario where the percentage of users uploading profile photos increases, but the number of users completing the sign-up process decreases. She stresses the importance of reviewing actual data to assess the business impact and make informed decisions. Emma explains that if the increase in engagement is significant, but there is also a significant drop in sign-up completion, it's a trade-off situation. Given the primary objective is to improve engagement, she recommends launching the feature despite the potential drawbacks. The paragraph concludes with advice on how to answer such open-ended interview questions effectively, emphasizing the importance of clarity, relevance, and context in metric selection.

Mindmap
Keywords
💡Magic Interview Question
A 'magic interview question' is a term used to describe a particularly insightful or revealing question that can help assess a candidate's skills and thought process in an interview. In the context of the video, it refers to a real metric interview question that the presenter believes is more helpful than simply sharing frameworks. The video aims to dissect such a question from a product management perspective, which is also applicable to data science interviews.
💡Frameworks
In the context of the video, 'frameworks' refer to structured approaches or sets of guidelines that can be used to solve problems or answer questions systematically. The presenter mentions that sharing frameworks alone might not be as helpful as discussing specific interview questions, suggesting that practical application and understanding of concepts are more valuable in an interview scenario.
💡Product Management Interviews
Product management interviews are a specific type of job interview focused on assessing a candidate's ability to manage and develop products. The video references a book about product management interviews, indicating that the strategies and questions discussed are relevant to this field. The interview process often includes evaluating the candidate's understanding of metrics and their ability to make data-informed decisions.
💡Data Science Interviews
Data science interviews assess a candidate's ability to analyze and interpret complex data sets, as well as their capacity to apply statistical methods and machine learning techniques. The video mentions that the book 'Decode and Conquer' is helpful not only for product management interviews but also for data science interviews, highlighting the relevance of understanding metrics and A/B testing in both fields.
💡A/B Testing
A/B testing is a statistical method used to compare the effectiveness of two versions of a webpage, product feature, or other interventions by dividing users into two groups and measuring their behavior. In the video, the presenter discusses an example of A/B testing where LinkedIn is testing a new feature that asks new users to upload a profile photo during the sign-up phase.
💡Profile Photo
In the context of the video, a 'profile photo' is a visual element that users upload to their online profiles to represent themselves. The video discusses a scenario where LinkedIn is testing a new feature that prompts users to upload a profile photo during the sign-up process, which is a change from the current process where users are asked to upload a photo after signing up.
💡User Engagement
User engagement refers to the level of interest and involvement a user has with a product or service. In the video, the presenter identifies user engagement as a key metric for evaluating the success of the new feature, suggesting that the goal of the feature is to improve engagement by having more users upload their photos during the sign-up process.
💡Metrics
Metrics in this context are quantitative measures used to assess the performance of a product feature or to track user behavior. The video emphasizes the importance of selecting appropriate metrics to evaluate the success of the new feature, such as the percentage of new users who upload their profile photos and the average number of invites sent per user per day.
💡Guardrail Metric
A 'guardrail metric' is a measure used to ensure that the pursuit of a new feature or change does not negatively impact other aspects of the product. In the video, the presenter suggests using a guardrail metric to monitor the percentage of users who drop off during the sign-up process, ensuring that this number does not increase as a result of the new feature.
💡Significant
In a statistical context, 'significant' typically refers to results that are unlikely to have occurred by chance, often associated with a p-value less than 0.05. The video uses the term to describe the expected outcomes of the A/B test, where an increase in user engagement or the percentage of users uploading photos should be statistically significant to validate the success of the new feature.
💡Fraud Detection
Fraud detection is the process of identifying, preventing, and addressing instances of deception or fraud. The video suggests that one of the potential goals of the new feature could be to improve fraud detection by adding credibility to new accounts through profile photos, as users with photos are less likely to be fraudulent.
Highlights

Emma discusses the value of sharing real interview questions over frameworks in her channel's content.

The video covers a real metric interview question from the book 'Decode and Conquer', useful for product management and data science interviews.

The original interview question involves evaluating the success of a new LinkedIn feature asking users to upload a profile photo during sign-up.

Emma emphasizes the importance of understanding the function of the feature in the context of the business goal before selecting metrics.

The goal of the new feature is hypothesized to be improving user engagement by having more users upload their photos during sign-up.

A secondary goal could be to detect fraud by adding credibility to new accounts through profile photos.

Three key metrics are proposed to evaluate the feature: user engagement, usefulness of the feature, and a guardrail metric.

User engagement is measured by the average number of invites sent per user per day or the number of posts made.

The usefulness metric is the percentage of new users who upload their profile photos, expected to be higher than the control group.

A guardrail metric monitors the percentage of users who drop off during the sign-up process to ensure it does not increase.

Emma advises on the importance of seeing real numbers and p-values to make data-informed decisions.

A scenario is presented where an increase in photo uploads is offset by a decrease in sign-ups, prompting a difficult decision.

The recommendation is made to launch the feature if more engaged users are deemed valuable despite the trade-off.

The candidate's approach to clarifying the question, selecting relevant metrics, and interacting with the interviewer is praised.

Different metrics could be chosen for the question, and providing 3 metrics is usually sufficient for an A/B test.

Metrics should be easy to measure and include a time threshold for clarity and practicality.

The importance of context in choosing metrics is stressed, to avoid sounding robotic and to make a convincing argument.

Emma invites feedback on the provided sample answer and encourages viewers to share their own approaches to the question.

The video concludes with an invitation for viewers to share their thoughts on the question and the approach taken in the video.

Transcripts

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Product MetricsData ScienceInterview TipsUser EngagementFeature TestingA/B TestingProfile PhotosLinkedIn StrategyTech InterviewPerformance Evaluation