Meta Layoffs — Which departments were impacted the most?
A brief analysis into the November 2022 Meta layoffs, examining which departments and levels of seniority were impacted the most.
Preface
Layoffs are a somber and saddening subject. Each datapoint making up this analysis represents someone who is currently in a tough situation, and I hope that everyone affected can find strength and land on their feet.
For the sake of anonymity of those impacted, I’ve removed all PII (Personal Identifiable Information) in the cleaned dataset that I’ve pushed to GitHub (link to repo here).
Background
On November 9 2022, Meta announced that it was laying off 11,000 employees, which translates to ~13% of their total headcount. What wasn’t publicized, however, was the distribution of the layoff across different departments. It was speculated that Recruiting and Business teams were impacted the most, but what do the actual numbers look like? How much was Engineering affected? What about Data Science and Analytics? How many juniors were affected compared to seniors?
Following the announcement on Wednesday, a public self-reporting MetaMates Talent Directory (public Google Sheets file) was created and shared on layoffs.fyi. At time of writing, this dataset contains over 2000 entries (nearly 20% of total layoffs, a decent sample to draw from). We can clean and compile this data to help answer some of these questions.
Findings
Observations
- Recruiting was impacted the most, but Engineering isn’t too far behind; together, these 2 departments comprise nearly 50% of all self-reported layoffs
- Engineering layoffs targeted mainly junior employees, whereas Product, Marketing, and Sales layoffs consisted mainly of senior employees
- The department who will need the most visa support is Engineering (46% of laid-off engineers will require visa support)
- Although Recruiting makes up the largest percentage of layoffs (26%), only 8% of them will require visa support
Assumptions and Limitations
- This analysis assumes minimal selection bias in our sample, which likely isn’t true. Some departments might be more/less likely to report their layoffs in the dataset, so our sample probably isn’t perfectly representative of the true population (all 11,000 layoffs).
- There is no validation/verification when contributing to this dataset. This is evidenced by “troll” records like “Rahul Ligma” (row 1723) and “Elon Musk” (row 1710). Luckily, datapoints like this are rare and unlikely to skew our insights.
💻 Link to Code (Jupyter Notebook)
- Moderate data cleaning had to be performed to categorize departments, since user-provided input wasn’t validated or sanitized. See code/comments for further details.
