Post Data Science Hype II
by Amy
We are Stella and Amy. We share firsthand stories and perspectives that are either lost in translation or simply inaccessible to you. Together, we bridge cultural divides and bring the world a little closer—one post at a time.
The Data Science Hype
If one can rank the most advertised jobs in history, data scientist would be at the top of the list. The famous 2012 Harvard Business Review article "Data Scientist: The Sexiest Job of the 21st Century" drew the attention of many young students and professionals to start a career in data science. I was one of them.
Don't get me wrong - I never regretted it and I love the profession. But time has given me a more pragmatic perspective.
Getting My Foot in the Door
Initially, I wanted to be an urban planner in transportation. However, the job prospects were dim. As an international student, I couldn't easily get a government job without citizenship. Even when I got interviews, the positions seemed dull and below my expectations.
Around that time, I heard about Jeff, a psychology major who had graduated a year ahead of me and transitioned into software engineering after a 3-month bootcamp. As a statistics senior, I came across the Harvard Business Review article declaring data science "the sexiest job of the 21st century." It felt perfect! I committed to becoming a data scientist.
Like many others who started data science careers in the early 2010s, I enrolled in a bootcamp to learn Python, machine learning, and SQL - the most sought-after skills in the field.
I was the youngest in my bootcamp and on my first data science team. The future looked bright! My confidence soared. I eagerly wanted to help others enter the profession. I genuinely enjoyed work each day, looking forward to new projects and finding the right statistical models for problems.
The Early Skepticism
As I established my data science career, one person was skeptical - my mother. She said it all sounded too good to be true. I talked about high salaries, relaxed work environments, and engaging projects. She questioned how long my job would last.
"Is it an iron rice bowl (鐵飯碗)?" she asked, referring to the Chinese term for guaranteed job security for life.
Of course it was! How could it not be? Data science would revolutionize business completely.
My mom worried my startup job wouldn't last. I was confident I could easily find another position after that future IPO. All industries needed data scientists - demand would be endless. It was a solid career!
She didn't buy it. "Everything is a cycle," she said.
The Party’s Over?
Fast forward to 2025. Data science jobs are at risk. We have "too many" new data science graduates. Companies, if hiring at all, want senior data scientists. The boom ended earlier than my mom predicted.
Analyzing why many data scientists failed to prove their value and now face job risks in 2025, I see three main reasons:
First, data scientists failed to meet the stakeholders’ expectations. For a data science project to succeed, it actually needs a lot of stars to align. Stella described a typical underwhelming data science project well in her last article.
Stakeholders often came with high hopes for immediate deliverables, unaware that data collection alone could take months. By the time data scientists reached the modeling phase, stakeholders might have already lost interest, and left with disappointment.
I’ve seen this cycle play out too many times. Data scientists would tirelessly explain why projects took so long while struggling to provide accurate timelines. The advice often given by experienced data scientists was to “start with low-hanging fruit.” However, “low-hanging fruit” usually meant building data dashboards, even better if they included tooltips and sliders. Stakeholders loved dashboards, but once you went down that path, it became increasingly difficult to pivot to more complex modeling work. What began as a simple “low-hanging fruit” often turned into a steady stream of dashboard requests, sidelining deeper data science ambitions.
In the end, neither party is happy.
Second, many data scientists focused on or were assigned to solve wrong problems. I often see people pursue dead-end projects. My analogy: if a car heads toward a cliff, upgrading its wheels or engine is pointless. Without sufficient user data or real product-market fit, why build models to optimize bottom-funnel experiences? Or in a poor ad market, good models can't squeeze out meaningful gains. Data scientists, even experienced ones, often get too absorbed in problems without analyzing their worth.
Third, soft skills now trump coding skills. Recent headlines have highlighted that AI is taking over software (data science) jobs, and to some extent, this is true. If you’ve ever built a model, you’ll realize that the core of the model often consists of just a few lines of code. The majority of the work is actually pre-processing and post-processing data, which AI can handle well if you know how to guide it. Therefore, the true differentiators for data scientists lie in soft skills such as domain expertise, storytelling, and persuasion, putting those who excel at technical skills at greater risk.
A Not-So-Positive Ending Note
People are anxious. Juniors get no callbacks and blame YouTube videos for luring them into this hyped profession. Senior data scientists and middle managers seek exit strategies, with most considering AI engineering or software engineering roles. Even those who seem to have stable data scientist jobs are developing backup plans through content creation or side hustles.
Personally, I ventured out to do something completely different. I announced that I am "leaving" data science to take on a more business development role because I need to be in a position of growth. After just 8 years of career in data science, I've learned that there is no such thing as the sexiest job.
All jobs are not sexy, some are better when the wheel of fortune turns. The real competency lies in good judgement.
Do read Stella’s take on Post Data Science Hype from last week. Let us know what you think!
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We are Stella and Amy. We share firsthand stories and perspectives that are either lost in translation or simply inaccessible to you.
I definitely feel your frustration, I'll write more on my own newsletter, but I'd strongly push back against blaming the victim here. My opinion is that the data bubble was explicitly a corporate creation from the very beginning, with a corporate agenda. Keep in mind that all the junior DS struggling for jobs now, they were literally just children back when when we worked at System1. They did everything their parents and professors told them. For the most part, they've held up their half of the bargain. Now what?