Are You Doomed as a New Technical Grad Entering Data Science?

Short answer: Absolutely not. But you need to be strategic.

If you're a recent technical graduate, you've probably heard the horror stories. CS grads facing a 6.1% unemployment rate (Federal Reserve Bank of New York, 2025). Computer engineering grads at 7.5% (NY Fed, 2025). Meanwhile, art history majors sit comfortably at 3% unemployment and philosophy majors at 3.2% (NY Fed, 2025).

The traditional "learn to code" path that promised guaranteed employment has hit a perfect storm: approximately 150,000 tech workers laid off across more than 525 companies in 2024 alone (Layoffs.fyi, 2024) are now competing for entry-level positions, AI tools are automating the repetitive coding tasks that entry-level developers used to do, and companies that once hired anyone who could write a for-loop can now demand years of experience for "junior" roles.

But here's what those discouraging headlines miss: to an outsider, it may seem that data science is facing the same crisis as software engineering - that generic "data science" roles are dying just like generic "software engineering" roles. However, that's not what's happening. While software engineering jobs are disappearing due to oversaturation and automation, data science roles aren't vanishing - they're evolving and specializing.

Why Data Science Is Different

The fundamental difference is that software engineering has become commoditized. Basic coding can now be learned quickly, automated by AI tools, or done by the thousands of experienced engineers who were laid off and are competing for entry-level positions. Companies no longer need to hire junior developers for repetitive tasks that AI can handle.

Data science, however, requires something that can't be easily replicated: deep analytical thinking. You can't automate the process of designing a proper statistical experiment, interpreting complex multivariate results, or understanding whether your model's predictions actually make business sense.

This is why data science is specializing rather than shrinking. Companies are realizing that the old "hire a generalist data scientist to do everything" approach was never sustainable. One data scientist creating business dashboards and another building neural network architectures are doing completely different jobs - imagine if we expected all "engineers" to be equally skilled at building bridges, designing microchips, and programming software.

So instead of generic "data scientist" postings, companies now hire for "ML Engineers," "Data Engineers," "Applied Research Scientists," and other specialized roles. This specialization is actually liberating for technical graduates because instead of competing to be a unicorn who masters statistics, software engineering, business communication, and cloud architecture simultaneously, you can pick your lane and excel there.

The numbers support this trend: job postings for people with AI skills reached an all-time high recently, with some 125,000 open jobs in the tech sector mentioning the need (CompTIA, 2025), with median annual salary for AI roles in Q1 2025 rising to $156,998 (Veritone, 2025). This isn't a dying field, it's a rapidly expanding one.

The Action Plan

1. Pick Your Specialization: Don’t try to be everything to everyone. Talk to people in different roles to get a feel for what each path involves, then choose one that genuinely interests you and go deep. Here’s a few specializations to look into:

• ML Engineer

• Data Engineer

• AI Researcher / Applied Scientist

• AI Engineer

• MLOps Engineer

• Cloud Data Architect

2. Learn the Right Stack for Your Path: The "modern stack" looks different depending on your specialization. AI engineers need PyTorch and cloud deployment tools. Data engineers focus on Apache Spark and distributed systems. Applied researchers might prioritize R, statistical libraries, and experimental design tools. Research what's actually used in your target role and focus there.

3. Build Production-Ready Projects: Skip the Titanic dataset tutorials. Build something that demonstrates you can deploy models, handle real data at scale, and create systems that actually work. Put it on GitHub and write about your process.

4. Network Strategically: Join AI/ML meetups, connect with alumni working in the industry. The field moves fast, and staying connected helps you spot opportunities early.

The Reality Check

Will you walk into your dream $170k data scientist job straight out of college? Probably not, unless you're exceptional. But neither will most people in any field. The key is getting your foot in the door and growing from there.

Consider starting as a junior ML engineer, data engineer, analytics specialist, or even a software engineer at a company doing interesting data work. These roles create natural stepping stones into senior data science positions while you build specialized skills and prove your analytical capabilities.

Why You're Not Doomed

Yes, the broader tech job market is brutal right now. But data science operates under different dynamics than traditional software engineering. While coding has become commoditized, data science still requires the analytical foundation that your technical degree provides.

The data science field isn't shrinking – it's professionalizing. Companies want people who can solve real problems with data, whether that's building production systems, designing rigorous experiments, or creating insights that drive business decisions. Your technical education gives you the exact foundation needed for this evolution.

While others are panicking about market saturation, you should be excited about the opportunity to enter a field that's becoming more sophisticated and better aligned with technical skillsets.

The question isn't whether you're doomed – it's whether you're ready to position yourself for the data science jobs of 2025, not 2015.

Sources:

https://365datascience.com/career-advice/career-guides/data-scientist-job-outlook-2025/

https://www.newyorkfed.org/research/college-labor-market#--:explore:outcomes-by-major

https://layoffs.fyi/

https://www.theregister.com/2025/06/30/ai_skills_job_postings_comptia/

https://www.veritone.com/blog/ai-jobs-growth-q1-2025-labor-market-analysis/

https://www.ue-germany.com/blog/the-future-of-data-science-as-a-career