Let’s be honest — platform engineering has always been an all-inclusive party. Ops folks, system admins, SREs, DevOps engineers, developers — all were welcomed guests. But suddenly, data scientists are getting the VIP treatment. It’s as if they’ve been handed the gold-plated invitation, complete with the best hors d’oeuvres. And for good reason.
Platform Engineers Rolling Out the Red Carpet
Take one look at how platform engineering has evolved in the age of AI, and the change is clear. According to a Red Hat “State of Platform Engineering in the Age of AI” report, platform tools, workflows and charters are increasingly being adapted to include developers, data scientists and AI practitioners — not just traditional devs or ops. On many internal platforms, MLOps specialists and data engineers are now alongside SREs and platform engineers, sharing turf.
While DevOps pipelines were once all about CI/CD for code, now we’re seeing unified DevOps + MLOps toolchains designed to wrangle ML models, data preprocessing and experimentation — an effort that requires bridging distinct worlds. Innovations like feature stores further accelerate model deployment and reuse, making data scientists’ lives smoother and more collaborative.
What’s Fueling the Obsession? (Hint: AI)
Let’s not disguise it: AI is the main stage, and data scientists are the headliners. With LLMs, generative AI and AI-first stacks accelerating at breakneck speed, organizations are bending over backwards to accommodate them. One report shows that 83% of enterprises have already integrated AI into their development stacks, and platform teams are hiring accordingly — adding AI engineers, data engineers and MLOps experts to their rosters.
Meanwhile, platform engineering itself is booming — CloudBees data shows 83% of practitioners are at least in progress in adopting platform engineering, stretching across cloud-native, DevOps, and cybersecurity teams. Platform teams are pivoting to support data science needs as a core part of their mission.
How Data Scientists are Feeling
So, what’s the data science reaction to this red carpet roll-out? Spoiler: Mostly glad, but wary.
The Job Market is Looking Good
The Bureau of Labor Statistics projects a 36% growth in data science roles between 2023 and 2033 — far more than average — and expects 20,800 openings per year. These roles are well-compensated: Median 2024 pay sits at $112,590, with top earners exceeding $194,000.
A 2025 analysis of job postings found that 32% of data science roles fall between $160k–$200k, another 27% between $120k–$160k. Meanwhile, the global data market is expected to hit $178.5 billion by 2025, growing at 26.5% CAGR — a reflection of the surge in reliance on data science.
Satisfaction, Tools, and Burnout
Insights from NASA veteran Chris Mattmann remind us that data science remains “in high demand,” but survival will require network support, mentorship and domain focus — not just code chops. And while automation like AutoML is rising, data scientists express mixed feelings — some fear replacement, but many remain optimistic that human-AI collaboration is the future.
Yet, real-world platforms lag. Studies show that DevOps and data science workflows often operate in silos, with communication gaps and tool mismatches that slow deployment and breed frustration.
Final Act: Enjoy the Limelight…While it Lasts
Here’s the thing: Data scientists are currently prom queens of the technology prom. Platform engineers, DevOps teams, security squads, cloud-native communities — they’re buzzing around like bees to honey.
But like high school royalty, this spotlight might fade. One day, the once-nerdy quantum engineer could be the apple of everyone’s eye at the 25th reunion. For now, though, dear data scientists, enjoy it. Bask in the attention, demand better tools, push for smarter integrations and make the most of your moment.
Long live the prom queen — because the prom is still in full swing, and everyone’s watching.