Meta Data Science, Analytics Screening: Interview Cheat Sheet

Hi, I’m Priscilla — a data scientist, previously at Yelp, now at TikTok. I participated in Meta Data Science, Analytics interview process in the past recruiting cycle.

Once you pass the recruiter phone screening, the first technical screen is designed to test your technical aptitude in 45 minutes. This is the critical round that will determine whether or not you move forward to an onsite.

In this article, I will provide a handy cheat sheet with frameworks and examples for your upcoming Data Science, Analytics screening.

The structure of the interview

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 minutes
• Interviewer: Senior/Staff DS
• Questions: Programming, Research Design, Data Analysis, Determining Goals and Success Metrics

Part 1. Programming (SQL)

Your interviewer will assess your ability to develop solutions to complex data problems using either Python or SQL. I chose SQL.

Example Question: Given a user activity table, find the number of users interactions since the user first log in to Facebook.

Part 2 and 3 belongs to the product-sense round. It is designed to screen your technical skills on AB testing, metric-sense, and product analytics.

Part 2. Data Analysis, Goals and Success Metrics

This section evaluates if you can structure an analysis plan to address a vague question. Your ability to identify metrics that reflect operational success and inform business objective is also tested.

Example Question: Facebook team is creating a notification that notifies a user when their Facebook Market listing is about to expire. How would you measure the quality of the notification?

• Display product understanding — “Facebook marketplace allows user to sell items on Facebook. Facebook would like to remind a user of an expiring listing since it will help users in managing listing and ensuring the quality and relevance.”
• State the goal of the feature — “The objective of the feature is to get users to update their listings or close irrelevant ones.”
• Scope impact — “Quality is vaguely define. A quality notification will be relevant, timely and does not spam the user. A quality notification will lead to feature success”
• Select and define key metrics — Quantify quality into measurable metrics. Write out the definition in Coderpad. (See code block below)
• Acknowledge the existence of segments — “The notification experience will vary by type of users. For example, a power user will receive far more notifications than the average user. The definition of notification success will also vary by function. Example, system notifications may have smaller open rate than a friend request notification.”
• Generate data for analysis — “We can collect notifications response data for updating a listing following the notifications. We can also see how Facebook Marketplace and overall activity of users who received the notification vary to those who didn’t”
• Translate statistic and ML concepts to practical applications— “We can generate insight from visualization or estimate impact of notification on user session (proxy for churn) with a regression analysis.”
• Interpretation of results — “If notification p-value is significant and coefficient is negative, it suggests that having a notification has a negative impact on user engagement.”
• Touch on trade-off — Facebook has a wide range of products. Consider cannibalization and how a users whole experience on Facebook is impacted by the feature change.