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[2023] Meta Data Science Interview (+ Case Examples)

Dan Lee / 2023-09-25

Aspiring to become a data scientist at Meta? A core pillar within the data science at Meta is the analytics role specialized in data analysis, data visualization, and AB testing. And, you have the opportunity to work across various products in the Meta ecosystem - Facebook, Instagram, Messenger, WhatsApp, Thread, AR/VR Devices.

Let’s look at a detailed guide on how to ACE the data scientist interview at Meta. Here are 7 key aspects to consider as you prepare for the data scientist interview.

  1. 📝 Job Application
  2. ⏰ Interview Process - Recruiter Screen/Technical Screen/Onsite Interviews
  3. ✍️ Example Questions
  4. 💡 Preparation Tips

1. Job Application

Getting your application spotted by a recruiter at Meta is tricky. There are, however, a number of strategies you can execute to maximize your chance of landing an interview.

1.1 Understand the role expectation

Meta has the following expectations about the role of the data scientist. Understanding their expectations provide clues on how the interviews will be structured in technical and behavioral rounds.


  • Explore large data sets to provide actionable insights with data visualizations
  • Track product health and conduct experiment design & analysis
  • Partner with data engineers on tables, dashboards, metrics, and goals
  • Design robust experiments, considering statistical and practical significance and biases

Soft Skills

  • Partner with cross-functional teams to inform and influence product roadmap and go-to-market strategies
  • Apply expertise in quantitative analysis and data mining to develop data-informed strategies for improving products.
  • Drive product decisions via actionable insights and data storytelling

You will see later, based on actual question examples, Meta places a great deal of assessing the candidate’s competencies in data preparation/analysis, product sense, experimentation, and stakeholder communication.

2.2 Tailor your resume

Tailor your resume to highlight the background and skills that recruiters and hiring managers look for:

  • Bachelor's degree (Master’s preferred) in Mathematics, Statistics, and a relevant technical field
  • Work experience in analytics and data science specialized in product
  • Experience with SQL, Python, R, or other programming languages.
  • Proven executive-level communication skills to influence product decisions.

This video guide will help you craft your resume👇 Data Science Resume Tips

2. Interview Process

The interview process at Meta can take 4 to 8 weeks. In some cases the entire process is expedited if you have a competing offer from another FAANG company (e.g. Google). The steps are recruiter screen → technical screen → onsite interview.

2.1 Recruiter Screen

The recruiter screen at Meta is usually formatted the following way:

  • 📝 Format: Phone Call
  • ⏰ Duration: 20 to 30 minutes
  • 💭 Interviewer: Technical Recruiter
  • 📚 Questions: Behavioral, Culture-Fit, Logistics

In the meeting expect to discuss the following:

  • On Meta’s mission and About the Role
  • Your Background - - "Walk me through your resume. Why do you want to work at Meta?”
  • Light Technical Questions - In some cases, a recruiter may ask simple statistics or SQL questions like explain the difference between INNER/LEFT/OUTER JOINS.
  • Your Logistics - Expect to discuss your visa/citizenship status, remote/hybrid/location preference, scheduling for the next interview.

Pro Tip💡- Practice explaining your story prior to the interview

2.2 Technical Screen

The technical screen at Meta is usually conducted on Coderpad, or a virtual pad where the interviewer will assess your coding and product sense ability.

  • 📝 Format: Video Conference Call
  • ⏰ Duration: 45 to 60 minutes
  • 💭 Interviewer: Senior/Staff DS
  • 📚 Questions: Data Manipulation (using SQL or coding) and Product Case

Pro Tip💡 - Practice 2 to 3 data manipulation problems up front when you are starting preparation with Meta. Aim to crack a problem within the 7 to 8 minute time limit.

2.3 Onsite Interview

1 to 3 weeks after the technical screen, you will be scheduled for the onsite stage. This is the most challenging aspect of the interview process. The bar is much higher than the technical screen.

  • 📝 Format: Video Conference Calls
  • ⏰ Duration: 45 to 60 minutes
  • 💭 Interviewer: Senior/Staff DS or Data Science Manager
  • 📚 Rounds/Questions: 4 to 5 Rounds - Programming, Research Design, Metrics, Data Analysis, Behavioral

Pro Tip💡 - Continue to ramp up on data manipulation skills and practice case problems verbally.

3. Interview Questions

Throughout the interview process, you will be assessed on a combination of the following areas:

📚 Areas Covered

  • Programming
  • Research Design
  • Determining Goals and Success Metrics
  • Data Analysis

3.1 Programming

The interviewer will assess you on on your familiarity with data manipulations (e.g. merging datasets, filtering data to find insights). Expect to discuss trade-offs in coding/query efficiency. Things to consider:

  • You will be given a choice of language to solve the problem - SQL, Python, R
  • It doesn’t matter which SQL (e.g. MySQL, PostgresQL) you are using as long as you know the common syntax
  • Explain your solution verbally as you write and/or after you are done.

📝 Here’s an actual question…

For each user, find the date of their first 'Login' activity and the total 
number of unique activity types they have engaged in since that first login. 
Exclude users who have never logged in. Order the result by userID.

| userID | activityDate | activityType  |
| 1      | 2023-01-01   | 'Login'       |
| 1      | 2023-01-02   | 'Comment'     |
| 2      | 2023-01-01   | 'Login'       |
| 2      | 2023-01-02   | 'Share'       |
| 3      | 2023-01-03   | 'Login'       |
| 3      | 2023-01-03   | 'Like'        |
| 3      | 2023-01-03   | 'Comment'     |
| 4      | 2023-01-04   | 'Login'       |
| 4      | 2023-01-05   | 'Login'       |



WITH FirstLogin AS (
    SELECT userID,
           MIN(activityDate) AS firstLoginDate
    FROM UserActivity
    WHERE activityType = 'Login'
    GROUP BY userID

SELECT f.userID,
       COUNT(DISTINCT ua.activityType) AS numberOfUniqueActivities
FROM FirstLogin f
JOIN UserActivity ua ON f.userID = ua.userID AND ua.activityDate >= f.firstLoginDate
GROUP BY f.userID, f.firstLoginDate
ORDER BY f.userID;

**Expected Output**:

| userID | firstLoginDate | numberOfUniqueActivities |
| 1      | 2023-01-01     | 2                        |
| 2      | 2023-01-01     | 2                        |
| 3      | 2023-01-03     | 3                        |
| 4      | 2023-01-04     | 1                        |

3.2 Research Design

Your interviewer is assessing design and explanation of AB testing and/or causal inference in various product cases. The general form of this question is formatted as follows:

  • Suppose we want to change feature [X] how would you design an experiment and test whether to change the feature or not?
  • What are the downsides of the methodology you propose? Are there biases in the analysis or experiment that we should correct for?

📝 Here’s an actual question…

The Messenger team proposes a feature that enables users to receive recent messages either unread or unresponded. How would you measure the effectiveness of this feature in an experiment?

👇 Here’s the solution

# Step 1 - Define Business Goal

Before diving into the experiment, it’s essential to clearly define the new 
feature's objective. For this feature, the objective is to increase user 
engagement by ensuring users do not miss or neglect important messages.

# Step 2 - Select Key Metrics

Primary Metrics:
> Response Rate: The percentage of users who respond to unread or unresponded 
messages after seeing the notification.

Secondary Metrics:
> Open Rate: The number or percentage of users who open the message notification.
> Retention Rate: Check if users who are exposed to the feature return to 
the app more frequently than those who aren’t.

# Step 3 - Experiment Design

Random Assignment: 

> Control Group: Users who do not receive the new feature.
> Treatment Group: Users who receive the new feature.

Ensure that these groups are randomly selected and that they’re statistically 
comparable in terms of demographics, user behavior, etc. Set the significance level 
at 0.05, statistical power at 0.80, and MDE at 1% relative lift from the 
baseline response rate.

# Step 4 - Run the Experiment

Run the experiment for 1 to 2 weeks to achieve the desired sample size, which is 
calculated based on the significance level, statistical power, and MDE.

# Step 5 - Launch Decision

Analyze the results:

> Check for statistical significance to ensure that observed differences 
are likely not due to chance. Check for the practical significance to see if 
the lift is meaningful for the business.
> Consider confounding variables or external factors that might have 
influenced the results.

3.3 Determining Goals and Success Metrics

Your interviewer wants to see your ability to define metrics that reflect success and inform business objectives. The question is usually formatted the following way: How would you measure [X] of a product [Y]? [X] is a quality like success, health, satisfaction; [Y] is any feature or product of Meta like Feed, Notifications, Instagram, and WhatsApp.

📝 Here’s an actual question…

How would you set goals and measure success for Facebook notifications?

👇 Here’s the solution

# Setting Goals for Facebook Notifications

> Enhance User Engagement**: The primary goal for notifications 
is to drive user engagement. They should prompt users to revisit 
the platform, engage with content, and participate in various activities.

> Personalization: Notifications should be tailored to individual 
user preferences, behaviors, and interests. This ensures they 
feel relevant and don’t annoy the user.

> Timeliness: Sending notifications at the right time can make a 
difference in user engagement. Analyzing user behavior to determine 
their active times is crucial.

> Maintain User Trust: It’s vital that notifications don’t become 
invasive or compromise user trust. They should be transparent, 
and users should have clear control over their preferences.
# Measuring Success for Facebook Notification

1. Click-Through Rate (CTR): This is the most direct measure. If 
users are clicking on the notifications and engaging with the app, 
it indicates the notifications are effective.

2. Engagement Time Post-Notification: Beyond just clicking, it's
 essential to see if users engage deeply with the platform after 
 accessing through a notification.

3. Conversion Rate: If a notification aims to drive a specific 
action (e.g., attending an event, using a new feature), 
the conversion rate will be a direct measure of its effectiveness.

4. Churn Rate Due to Notifications: Monitor if there's an increase 
in users turning off notifications or if there's an uptick in 
app uninstalls after a notification. This could indicate that 
the notifications are too frequent or not relevant. 

5. User Feedback: Direct feedback about notifications, gathered 
through surveys or user interviews, can provide qualitative insights.

6. Segmented Analysis: Different user segments may react differently.
 It's essential to measure success metrics across various user 
 segments to ensure notifications are effective for the entire user base.

3.4 Data Analysis

Your interviewer will be looking for how you leverage methods ranging from descriptive statistics to statistical models to answer hypothesis-driven questions. Things to consider are the following:

  • What are the hypotheses that would lead to a decision? How would you prove a hypothesis is true?
  • Can you translate concepts generated into a specific analysis plan? Are you able to use data to answer the original question posed with enough detail to demonstrate the ability to execute on an analysis?

📝 Here’s an actual question…

How would you measure the impact of parents being on Facebook on teenagers?

👇 Here’s the solution

1. Define the Scope of 'Impact': Before diving into measurements, 
it's crucial to clarify what "impact" means. Does it refer to:

- Content consumption behaviors (e.g., what teenagers view or read)?
- Content creation behaviors (e.g., what teenagers post or share)?
- Social behaviors (e.g., whom they interact with)?
- Privacy and security settings?

2. Data Collection and Preparation:

Gather historical data on the behaviors of teenagers on Facebook. 
This could include:

-Frequency of posts, likes, shares, and comments.
- Privacy settings adjustments over time.
- Types of content consumed.
- Duration and times of active sessions

Generate relevant features that can serve as explanatory variables. 


- Parent_Active: Binary variable indicating whether a parent 
is active on Facebook (1 for yes, 0 for no).
- Average_Session_Time: Average time a teenager spends on 
Facebook per session.
- Number_of_Family_Friends: Count of family members or family 
friends in their friend list.

3. Set up Regression Analysis:

You'll want to quantify the impact of parents being on Facebook 
on a specific teenager behavior. For instance, if you're 
interested in the number of posts:

- Number_of_Posts ~ B0 + B1 * Parent_Active + B2 * Average_Session_Time + B3 * Number_of_Family_Friends
- Ultimately you are measuring the effect of Parent_Active on teenagers. 
The target variable Number_of_Posts could be swapped with other
behavioral metrics of a teenager - let’s say Number_of_Likes.

4. Interpretation:

Evaluate the coefficient of the Parent_Active variable:

- If it's significant and negative, it suggests that having 
parents on Facebook decreases the behavior in question (e.g., 
number of posts).
- If it's significant and negative, it suggests the opposite.
- If it's not statistically significant, you can't confidently say that
parents' presence on Facebook influences that particular behavior.

4. Preparation Tips

Use the following resources to further help your prep!