Moyan Chen was 24 years old and living her dream — a data scientist at Meta, working on Instagram in New York City. Less than a year earlier, she'd beaten thousands of candidates to land the coveted role. It felt like the ultimate validation of years of studying and grinding.
Then the rumors started in March. Whispers about layoffs circulated through the office. Nobody knew when the axe would fall, but everyone felt it coming.
"Some of my colleagues and I were dreading Wednesdays because Meta has sometimes laid people off on those days," Chen recalled. "Every Tuesday night when I left work, I wondered if I was coming back."
The ritual became unbearable. Every Wednesday morning, she'd wake up early and check her email, heart racing, looking for that dreaded termination notice.
This went on for weeks. Then in April, a date was set: May 20.
When the day finally arrived, the feeling surprised her. "When the day finally came, and I got laid off, I was like, 'This is it.' It was more like relief than pain."
The waiting had been worse than the outcome.
Chen believes AI played a central role in her dismissal. Meta was restructuring around AI — shifting headcount and budget toward AI infrastructure and away from traditional data roles. The writing was on the wall for routine data work.
"I feel like, ultimately, I lost my job to AI," she said.
She saw it firsthand. AI-generated SQL queries had become so accurate that she stopped checking them. Writing queries, creating visualizations, analyzing metrics — the bread and butter of a junior data scientist — were already being automated. "It got to the point where I wouldn't check AI-generated queries because they have gotten so accurate. I thought that if AI made a mistake on a specific task, I would make 10."
The layoff shattered her belief that a big-tech job was a safe career choice. "Now I feel like it's not safe anymore, like I can get laid off at any time."
She's not sure what comes next. She's exploring career coaching, sharing her journey online, and considering joining an AI startup — risky, she acknowledges, but staying in traditional data analytics feels riskier in the long run.
"Staying at a big company doing traditional data analytics and reporting jobs just feels like I will be left behind. That's riskier in the long term."
Her advice to young data professionals: "If you only know how to code, that's not enough. If you're just writing SQL queries, using Python, or tracking and analyzing metrics, it's not a very promising career anymore."
Future data scientists, she believes, will need broader skills — business understanding, product sense, strategic thinking — not just technical expertise.