2024 美國地獄模式上岸DS/MLE經驗分享(貳) — 如何準備Meta Product Case面試
這篇主要分享我在準備Meta DSA (Data Scientist, Product Analytics)面試的經驗和心得，這次找工作為了Meta的面試在Product Case和A/B Testing上下了不少工夫，雖然最後沒有拿到Offer略有遺憾，在當中還是學習了許多，希望可以分享給想找偏Product, Analytics方向的Data崗位的朋友。
⚠️由於跟Meta簽了NDA(nondisclosure agreement)，我無法透露自己在面試中所遇到的具體題目，更多著重於分享我準備時整理的資料、準備心得。
本文Outline：
貳、如何準備Meta Product Case & A/B Testing Interview
* 一、Meta DSA 面試流程和內容介紹
* 二、Product Case 面試要點、框架
* 三、A/B Testing 面試要點、熱門考題
* 四、Meta Product Case 實戰：熱門考題
* 五、Product Case & A/B Testing 面試準備資源分享一、Meta DS面試流程和內容介紹
我的Meta DSA面試時間線如下：
- 1/27：內推
- 1/29：HR email reach out
- 2/2：HR Call
- 2/28：Tech Screen，結束後半個小時HR就打來說通過了。
- 3/18, 3/21：VO (Virtual Onsite)
- 3/26：Rejected, but “happy to reach out to any other teams for roles you may be interested in.”
整個過程花了約兩個月，如果VO通過，進入Team Match階段的話可能還需要再花2–3週的時間，HR在我通過Tech Screen的時候就跟我說如果順利的話最後Team Match確定的時間大概是4/8 or 4/15。有人可能會問我為什麼我Tech Screen跟HR Call間隔了3.5周，Tech Screen跟VO之間又間隔了2.5周。首先Tech Screen的部分HR就是跟我建議至少花兩週時間準備，Meta的HR非常貼心，給了很多面試準備資源，甚至加了我LinkedIn把一些preparation resource link發給我。
我自己在安排面試時間，提供availabilities的時候，主要考量就是：①希望面試時間晚一點，可以準備得更充分。②希望面試時間快一點，避免Headcount沒了。我還記得2/2 HR Call的時候HR跟我說至少建議準備兩週，但是盡量不要到3月，因為確實可能拖太晚就沒有缺了。因此自己得在這兩個考量中取得一個平衡點。我個人面試下來的心得是，投入多少時間準備Meta的面試，對於面試表現還是影響很大的。原因是Meta基本都是考面經題，因此有沒有準備過題目差別很大，若能針對面經題全面考量不同的問法、準備不同的follow up questions會有相當大的幫助。另外考慮到其他candidate也都準備的很充分，想要得到不錯的勝出機會通常需要花上至少相等或更多的努力。（你是天才的話就當我沒說）
Meta DSA面試內容介紹：
這個部分其實網上有很多寫得很好的文章了，我在此簡短說明並以幾個面經題舉例。Meta的DSA面試其實流程相當簡單，先是一輪Screen Interview，過了就是VO，因此就針對這兩輪面試來做介紹：
1. Screening Interview
Interview Overview (45 mins in total)：
* Programming
* Research Design
* Determining Goals and Success Metrics
* Data Analysis其實可以簡單歸納為兩個部分 — SQL & Product Case Questions，SQL部分通常是一個題組，提供幾個Table，讓你寫SQL回答幾個問題（Recruiter在安排面試之前有問說能不能用SQL回答，不行的話也可以選擇Python or R，不過很少candidate會選）。至於Product Case Question的部分，either是接著SQL題組往下討論，或著就是提供一個全新的Case進行討論。
直接以一畝三分地的面經改編作為例子吧：
SQL部分：
Table: messages
sender_id | receiver_id | send_date | message_id | message_length
----------|-------------|-------------|------------|---------------
101 | 202 | '2023-04-01' | 1001 | 150
102 | 203 | '2023-04-01' | 1002 | 200
101 | 204 | '2023-04-01' | 1003 | 120Table: users
user_id | signup_date | country | primary_device | active_flag | last_active_date
--------|-------------|---------|----------------|-------------|-----------------
101 | '2022-01-01'| US | Android | 1 | '2023-04-01'
102 | '2023-01-02'| GB | iOS | 1 | '2023-04-01'
203 | '2022-12-15'| US | iOS | 1 | '2023-03-30'
204 | '2022-11-10'| CA | Android | 0 | '2023-03-25'- Q1: Calculate the average message length sent by active users in the last 30 days.
SELECT AVG(message_length) AS avg_message_length
FROM messages m
JOIN users u ON m.sender_id = u.user_id
WHERE u.active_flag = 1 AND m.send_date > CURRENT_DATE - INTERVAL '30' DAY;- Q2: Identify the top 5 users who sent the most messages in the last month.
SELECT sender_id, COUNT(*) AS message_count
FROM messages
WHERE send_date > CURRENT_DATE - INTERVAL '30' DAY
GROUP BY sender_id
ORDER BY message_count DESC
LIMIT 5;- Q3: Determine the percentage of messages sent to users within the same country.
SELECT ROUND(
(COUNT(*) FILTER (WHERE u1.country = u2.country) * 100.0) / COUNT(*), 2)
AS percent_within_same_country
FROM messages m
JOIN users u1 ON m.sender_id = u1.user_id
JOIN users u2 ON m.receiver_id = u2.user_id;Product Case部分：
- A) Defining Quality in User Engagement:
How do you determine what constitutes effective user engagement for digital notifications? - B) Impact Assessment of Features:
What general steps would you take to evaluate the potential impact of a new feature that aims to increase user interaction? - C) Principles of A/B Testing:
Can you outline the key components of an A/B testing strategy? What metrics would you typically consider? - D) Analyzing User Behavior:
How would you analyze whether a feature has successfully influenced user behavior? - E) Mitigating Bias in Tests:
What strategies do you use to minimize bias in experimental designs, especially in environments with strong social connections? - F) Evaluating Significance of Results:
After an experiment, how do you determine the significance of your findings? What additional checks would you perform if the results indicated a major change? - G) Diagnosing Performance Issues:
What process would you follow to diagnose unexpected changes in performance metrics?
推薦下面這篇寫Screening面試的文章，寫得相當不錯：
2. Virtual Onsite
Interview Overview (45 mins each):
* DS Technical Skills
* DS Analytical Execution
* DS Analytical Reasoning
* DS Behavioral
# 這是Meta在2022一月左右面試改革後的模式，之前略有不同。VO分為四個面試，分別側重於不同的面向，每一個面試45分鐘，可以安排在同一天或是multiple days。
a. DS Technical Skills
這個部分基本就是考SQL，跟Screening考SQL的形式差不多，如果有多餘的時間面試官可能也會follow up問一些Product Question。
b. DS Analytical Execution
這個部分主要是考一些基本的統計知識、計算，可能也會延伸考一些跟Product Sense有關的問題。
一樣拿一畝三分地的面經改變舉例子：
Scenario: Digital News Platform
Background:
A digital news platform is experimenting with two models for integrating promotional content into articles to balance revenue generation with reader engagement.
- Model A: Randomly integrates promotions with a 5% chance in any article.
- Model B: Ensures every 20 article contains a promotion.
Questions:
- Q1: For a set of 200 articles, calculate the expected number and variance of promotions for each model.
- Q2: What is the probability of encountering back-to-back promotional articles?
- Q3: Outline an experiment to determine which model leads to better reader retention. Metrics could include time spent on page and bounce rate.
c. DS Analytical Reasoning
這個部分就是Screening裡面的Product Case Question，相對Screening時間拉長，會有更多的追問、探討分析細節。
d. DS Behavioral
General behavioral question interview, e.g.
- How did you help a new member feel more belonging to the team?
- How do you work with people you’ve never worked with before?
下面這篇文章對於Meta DSA VO介紹的也相當清楚：
二、Product Case 面試要點、框架
關於Product Case Interview，我在2021年的時候就寫過一篇文章：
回頭再看，我覺得大部分概念還是不錯的，文章里提到的解題框架也相當有趣，只是可能還能更全面。Product Case是開放性的題目，沒有標準答案，因此這類題目我認為關鍵在於你能否展現你的Product Sense（對產品、業務的理解、熟悉度），分析方法邏輯性強不強，框架性思維是否完整、全面，實驗設計考量是否周全，如何提出假設和進行驗證⋯。
除了前面推薦Priscilla Mannuel寫準備Screening Interview的文章以外，幾篇一畝三分地寫Product Case Interview的文章也相當值得參考：
引述其中一些相當有收穫的內容：
- “把trade-off 講清楚，是你能否拿到senior的關鍵。Junior pursues right or wrong; Senior looks for trade-offs.”
- “我的架構大致上為：FB mission (business goal) → Product goal (pain point solving / why are we doing this) → Hypothesis → Validation (Metrics and methodology i.e experiment design) → Analysis → Decision”
- “不要等到INTERVIEWER 問你再說！PROACTIVELY 意思是你自己DRIVE CONVERSATION 而不是讓INTERVIEWER 擠牙膏一樣的問你”
- “這一點不管SENIOR 還是JUNIOR 都適用，就是答題要有邏輯”
再看這幾篇文章，實在是寫得相當好，不如大家就看看他們寫的吧😂，尤其第一篇寫得已經很全面了，我們直接跳到實戰題。
三、A/B Testing 面試要點、熱門考題
對於大部分Product DS, DA, BA來說，A/B Testing都是面試重點中的重點，尤其是在互聯網、Tech大廠，許多公司都相當地A/B Testing Driven。Meta的DSA面試尤其是如此，因此在此特別整理了A/B Testing面試中熱門考點和題目分享給同學。除了最基本的A/B Testing流程以外，其實通常A/B Testing面試主要考的重點不外乎：
A) Network Effect, Split Strategy/Randomization Unit (Reason of Choice, Comparison and Drawbacks)
- Evaluating Randomization Approaches:
“Discuss how different randomization strategies might affect the outcomes of an online platform’s feature testing. What are the pros and cons of using geographical versus individual-level randomization?” - Impact of Randomization Choice:
“What challenges might arise from incorrectly chosen randomization units in an online experiment? Provide an example of how this could potentially skew the results.”
B) Novelty Effect
- Identifying Novelty Influence:
“How can a tester discern whether positive feedback on a new platform feature is due to its novelty rather than its utility?” - Strategies to Counter Novelty Bias:
“What methods can be employed in the design of an A/B test to reduce the influence of novelty on user behavior metrics?”
C) Practical Significance, Considerations, Decisions
- Statistical versus Practical Significance:
“Describe a scenario where the statistical significance of a test result might not align with practical business goals. How would you handle such a situation?” - Decision Making with Borderline Results:
“How would you approach decision-making when an A/B test result is statistically significant but the effect size is small?”
D) Testing Long Term Effect
- Design for Long-Term Impact Evaluation:
“What are key considerations when designing tests to measure the long-term impact of a new feature on user engagement?” - Long-Term Monitoring Post-Implementation:
“How do you plan ongoing evaluations of a newly implemented feature to assess its sustained impact over time?”
E) Running Multiple Test at the same time
- Handling Interactions in Multiple Concurrent Tests:
“What precautions should be taken when conducting several A/B tests simultaneously to avoid interference among them?” - Analyzing Results from Concurrent Testing:
“How do you interpret the results of one positive outcome among numerous concurrent A/B tests?”
F) Estimate Sample Size / Experiment Duration (Ensure Power)
- Sample Size and Test Duration Calculation:
“What factors influence the determination of sample size and duration for an A/B test in an e-commerce setting?” - Early Significant Results in Testing:
“How should a team proceed if significant results are observed earlier than expected in an A/B test? What are the risks of stopping a test prematurely?”
由於自己不是什麼A/B Testing大師，這裡就先略過解答，後面提供一些A/B Testing相關的優質文章和資源給大家參考。
四、Meta Product Case實戰：熱門面試題匯總
在此直接分享我在準備Meta Product Case Interview時最常看到的四個Case題組。雖然這些題組可能不是我自己在面試時遇到的，但是還是屬於比較敏感的信息，因此稍微改編了一下題目。相信這些題目對於準備Meta DSA, 或著其他家Product Data Scientist, Business Analyst的同學還是非常有幫助的，同學可以很好的練習到如何以一個DS的角度去解決實際的Product Case，在面對商業問題的時候如何考慮的比較周全，可能會遇到什麼樣的Follow Up Questions。
A) Implementing Recommendation Notifications in a Video Streaming Platform
Background:
You are part of a product team at a video streaming service that is considering launching a new notification strategy. The service wants to introduce recommendation notifications, which alert users when a new show matching their watch history is available or when a show they are watching is about to release a new season.
Notification Types:
- Personalized Show Recommendations: Notify users based on their viewing history.
- New Season Alerts: Notify users when a show they’ve watched updates with a new season.
Case Questions:
- Defining Notification Quality:
How would you define high-quality notifications in the context of a video streaming service? What data points would you need to gather to ensure the notifications are both relevant and timely? - Strategic Implementation:
What would be your strategy to introduce these recommendation notifications? Consider the initial rollout to a subset of users through A/B testing. - Metrics to Measure Success:
Beyond click-through rate (CTR), what other metrics would you consider essential to evaluate the effectiveness of these new notifications? How would you measure the impact on user retention and engagement rates? - Experimentation and Evaluation:
How would you set up an A/B test for these notifications? What would be your criteria for success, and how would you choose your randomization unit?
How would you track whether these notifications lead to increased viewing or subscription renewals? - Handling Potential Declines:
If after launching these notifications, you observe a decline in CTR or other engagement metrics, how would you investigate the causes? What steps would you take to determine if this decline is due to notification fatigue, irrelevance, or other factors? - Network Effects and Social Sharing:
Considering the potential for users to share their viewing experiences, how might network effects be leveraged or accounted for in the design of your A/B test? What implications could social sharing have on the effectiveness of your notifications? - Long-term Considerations and Adjustments:
Once you have initial test results, how do you plan to refine and optimize the notification strategy? What additional checks would you perform if the initial results indicate significant changes in user behavior?
B) Implementing a Local Event Recommendation System in a Social Media Platform
Background:
Imagine you are a product manager at a social media company planning to introduce a new feature within the platform: a recommendation system that suggests local events to users based on their interests, previous interactions, and network activity. This feature aims to enhance user engagement by making the platform more interactive and community-focused.
Scenario:
The company wants to leverage its data on user behavior, location, and social connections to curate and recommend events that users might find interesting, such as concerts, meetups, or local festivals.
Case Questions:
- Defining the Business Goal and Engagement Metrics:
What are the primary business goals for introducing the local event recommendation system?
Which engagement metrics would you define to measure the success of this recommendation system? - Data Requirements and Model Building:
What types of data would you need to collect to power this recommendation engine?
Describe how you would use machine learning to model these recommendations. What features would you consider, and why might you choose a particular model type (e.g., collaborative filtering, content-based filtering)? - Validating the Recommendation Model:
How would you validate the accuracy and relevance of your model’s recommendations before a full rollout?
Discuss the methods for A/B testing this feature. What key metrics would you track to evaluate the changes in user behavior? - Addressing Bias and Network Effects:
What hypotheses can you formulate for detecting bias in the recommendations?
How would you test for and mitigate potential network effects that might skew the recommendation results? - Evaluating System Impact:
How would you assess whether the new recommendation system is a net positive for the platform or if it’s cannibalizing existing features (e.g., general news feed engagement)?
If data shows that engagement with recommended events posts increased by a certain percentage but overall platform engagement slightly declined, how would you analyze and respond to these findings? - Implementation and Rollout Decisions:
When and how would you decide the best times to inject these recommendations into the user’s news feed?
Based on testing results, how would you determine the timeline and strategy for a full-scale rollout?
C) Launching a Shopping Feature on a Social Media Platform, “SocialShop”
Background:
Imagine you are a product manager at “SocialShop,” a new social media platform that aims to integrate a shopping feature, enhancing the interaction between users and businesses. The feature is in conceptual stages, and your task is to outline the strategy for identifying potential users, launching the feature, and measuring its success.
Scenario:
“SocialShop” is primarily a lifestyle and fashion-focused platform. The shopping feature intends to allow users to purchase directly from a business page, tagged products in their feed, or a dedicated shopping tab where they can explore new products.
Case Questions:
- Identifying Potential Target Users:
How would you identify users who are likely to be interested in the new shopping feature on “SocialShop”? What data points would you consider to predict user interest in shopping? - Feature Launch Strategy:
Describe the customer funnel for the shopping feature. What steps would users typically follow from discovering a product to making a purchase?
How would you differentiate this shopping experience from competitors, like Instagram Shopping or Facebook Marketplace? - Key Metrics for Success:
What are the top metrics you would track to evaluate the success of the shopping feature? Consider metrics related to both buyer engagement and seller activity.
How would you use these metrics to determine if the feature is meeting its business objectives? - Improving the Shopping Experience:
Propose ideas to enhance the daily number of purchases per user. What kind of data would you analyze to find potential growth areas?
How would you balance focusing on attracting new buyers versus supporting existing sellers? - A/B Testing Strategy:
How would you design an A/B test to measure the effectiveness of different aspects of the shopping feature, such as product list visibility or the ease of the purchasing process?
What would you consider a successful outcome of these tests - Evaluating Trade-offs:
Suppose initial data shows an increase in revenue per user but a decline in retention rates. How would you investigate and address these trade-offs?
How would you assess whether the shopping feature is a net positive for the platform, considering both its impact on revenue and user engagement? - Long-term Strategy and Adjustments:
Based on the initial performance, how would you plan to scale or adjust the feature to better fit user needs and business goals?
Discuss potential next steps if the shopping feature significantly alters the dynamic of the platform, either positively or negatively.
D) Launching “Live Group Hangout” Feature on a Digital Communication Platform
Background:
Imagine you’re part of a product team at “CommuniTech,” a company that offers a multifaceted digital communication platform. The platform is considering introducing a “Live Group Hangout” feature that allows users to engage in live video interactions while participating in interactive activities like games, quizzes, and collaborative projects.
Scenario:
The new feature is designed to cater to a diverse user base ranging from young adults seeking social interaction to corporate teams looking for casual engagement opportunities. This feature aims to deepen user engagement by providing a more interactive and fun experience compared to traditional video calls.
Case Questions:
- Defining the Concept and User Needs:
What unique needs does the “Live Group Hangout” feature meet for different user demographics? How does it enhance their experience beyond standard video calls?
What initial research methods would you employ to assess the potential interest in such a feature among existing users? - Feature Specifications and Design:
Considering the technical and social aspects of live interactive sessions, what key features should the “Live Group Hangout” include to appeal to its intended user base?
How would you incorporate elements of gamification and collaboration into these hangouts to ensure they are engaging and retain user interest? - User Segmentation and Targeting:
How would you identify and segment users who might be early adopters of the “Live Group Hangout” feature? What data points would you analyze to target these users effectively? - Pilot Testing and Metrics:
Design a pilot test for the “Live Group Hangout” feature. What would be the structure, and what specific metrics would you track to evaluate its success?
How would you measure user engagement, satisfaction, and repeat usage during the pilot phase? - Feedback Loop and Iteration:
Discuss the feedback mechanisms you would implement to gather real-time user impressions and suggestions during live sessions.
How would you use this feedback to iterate on the feature before a broader rollout? - Scaling and Integration Challenges:
What challenges might arise when scaling the “Live Group Hangout” feature for a larger audience?
How would you integrate this feature into the existing platform infrastructure to ensure seamless user experience and minimal disruption? - Long-term Viability and Expansion:
Assuming the feature is successful, outline potential expansions or spin-offs that could further enhance user interaction on the platform.
How could the insights from user behavior in “Live Group Hangouts” inform other areas of product development at “CommuniTech”?
我自己在準備的時候，有跟一些同時在準備Meta面試的朋友Mock Product Case，也有自己把面經題彙整，包括各種可能的Follow up questions，然後寫下回答的Notes。我覺得自己寫下來、釐清思路對面試幫助很大。由於本篇提供的信息量可能已經過剩，我自己的參考解答就留到後續有機會再分享吧。
五、Product Case & A/B Testing面試準備資源分享
整理推薦的準備資源，有一些上面已經提過了。
經過三年，我仍然推薦<A Collection of Data Science Take-Home Challenges>。這是前AirBnb資料科學家Giulio Palombo整理的一份資料科學家面試經典題庫，其中包含20個Take-Home Challenges，40個Product Questions以及6個SQL題組，有興趣的人可以直接到Data Masked官網了解：https://datamasked.com
A/B Testing相關推薦文章：
以下兩篇應該也不錯，有點忘了，有時間的話可以看看：
Meta Product Case答題技巧和框架：
Meta面試形式介紹和經驗分享：
其他Product Interview相關推薦資源和文章：
