Is data scientist still the sexiest role in IT?
Way back in 2012, Harvard Business Review dubbed the role of data scientist the sexiest job of the 21st Century. Organizations scrambled to find skilled data scientists in a tight job market, and IT professional raced to obtain degrees and certifications in data science and analytics in order to boost their job options and earnings potential.
So is the data scientist role still as in-demand as it was then? Well, yes and no. While the answer depends on who you ask, conditions have definitely changed in the data scientist job market. Consider the following observations:
Rapid growth: “The data science market is experiencing rapid growth and expansion. We are seeing businesses from all industries and all sizes increasing their data teams across the board, including data scientists,” explains Matt Collingwood, managing director at recruitment agency VIQU IT Recruitment.
Strong and steady: “Overall, the market remains strong and resilient, even in a time of economic uncertainty and tech-sector layoffs,” says Art Zeile, CEO of Dice, a tech careers marketplace. “According to recent 2025‑market analyses, demand for data science continues to hold steady, especially in organizations doubling down on AI, ML, analytics and data-driven decision making.”
Cooling off: “It’s no longer a guaranteed golden ticket,” explains Elizabeth M. Harders, career strategist and executive resume writer at Resume Polished. “The demand is still there, but the market has cooled slightly, especially for mid-level roles. Entry-level candidates are struggling more than ever to land interviews, while senior-level scientists are being expected to wear multiple hats, from data wrangling to storytelling to product strategy.”
Dynasty in decline: “The data science job market is oversaturated, with too much talent competing for a shrinking pool of opportunities due to a slowing economy and the impact of AI, which is eroding traditional data science skills,” says John Bates, CEO at SER Group, developer of a workplace collaboration platform. “Making this worse is the fact that many candidates, drawn by the promise of six-figure salaries, possess great generic credentials but can’t show a strong engineering or computing background.
Trends driving current demand for data scientists
Demand for data scientists today is being driven by several interrelated trends, Zeili explains. First is the growing adoption of AI and machine learning across business operations, product development, marketing and strategic decision-making. Employers are no longer looking for professionals who can simply analyze data; they want orchestrators who can build, deploy and manage machine learning models in production environments, Zeili explains.
At the same time, data science is becoming more deeply embedded in go-to-market functions such as marketing and sales, where analytics help drive growth and inform decision-making, Zeili says. This has led to a broader set of expectations for candidates.
“The ideal data scientist is now a versatile professional who can navigate everything from data cleaning and modeling to engineering pipelines and translating technical insights into business strategy,” Zeili says. “As organizations move toward cloud-first and big-data architectures, familiarity with infrastructure, data engineering, and cloud platforms is essential for a data scientist.”
These drivers are calling for data scientists with more advanced skillsets – both in terms of technology and business strategies. The shift is from big data to actionable insights — companies want data professionals who can influence business outcomes, not just build models, Harders explains. This also means more scrutiny on ROI — executives are asking, ‘What’s the tangible business value of this model?’
Still attractive, but in a different way
Data scientists continue to be among the most sought after IT pros, but not in the same way, Harders says.
“Data scientists were unicorns: rare, expensive, and transformative,” Harders explains. “But now the novelty has worn off. The appeal has shifted to roles that translate data into action, think data product managers, analytics leaders, and ML engineers.”
It’s still critical work, but it’s no longer romanticized. The field is maturing. Companies want ROI, efficiency, and collaboration, not just technical brilliance.
Zeili agrees. “Data science has been and continues to be one of the sexiest jobs in many respects. Back when Harvard Business Review coined “data scientist: the sexiest job of the 21st century,” the role was fresh, rare and in high demand — combining statistical, programming and business‑analytical skills in a way few others did.”
Today, the allure remains. Demand persists, and data scientists are among the highest-paid and most influential technical professionals. But the nature of ‘sexy’ has changed: it’s less about novelty and more about impact and business integration.
“That said, the rise of AI and automation tools has sparked debate. Some argue foundational tasks that once defined the data‑scientist role — data cleaning, basic analytics, even some modeling — can now be automated or assisted by AI,” Zeili explains. “So yes, the role is still ‘sexy,’ but its value is evolving. The most sought-after data scientists now are those who bring advanced, specialized skills and business‑savvy, not just technical chops.”
The new hot role is still emerging
As the role of data scientist has become less novel, what will take its place? The emerging role of the chief artificial intelligence officer (CAIO) might, Bates says.
“For AI to be more than a novelty, it has to be embedded in your company’s proprietary and protected institutional memory. And to make that happen, I predict we’ll soon see leaders whose sole focus is managing AI,” Bates explains.
Like it or not, AI is now capable of doing a substantial amount of what data scientists do, Bates explains. Certain tasks traditionally performed by data scientists, such as data preparation, basic analysis, or routine modeling, are increasingly being automated.
That said, contributions requiring domain expertise, oversight, integration, and governance are likely to remain important or even become more critical. And this is exactly where the CAIO comes in, Bates says.
“Their remit extends far beyond the boundaries of a traditional data science function,” Bates says. “A CAIO isn’t just optimizing models; they’re setting strategy, ensuring ethical deployment, integrating AI into the organization’s institutional knowledge, and governing how AI is used across the business. It’s a role I believe will become genuinely strategic and transformational.”
As AI and automation handle more repetitive tasks, such as data cleaning, data scientists are expected to take on more strategic and value-adding responsibilities. Soft skills and stakeholder management, which AI can’t currently undertake well, are becoming more important for data scientists, Collingwood says.
Data scientists are still in-demand
Compensation remains strong for data scientists, Zeili says. According to recent job‑market analyses, many data science roles now offer salaries in ranges between roughly $160,000 to $200,000, with a significant portion also falling just under that – e.g. $120,000 to $160,000 – depending on experience, location and role focus.
“Because of high demand, especially for those with machine learning, artificial intelligence and data‑engineering skills, top-tier candidates can command even more. Per our 2025 Tech Salary Report, AI skills demand a premium of up to 18%,” Zeili says.
Benefits beyond base pay, such as bonuses, equity, remote/hybrid work flexibility and opportunities to work on cutting‑edge AI or ML projects, also remain part of what attracts and retains strong data science talent, Zeili explains. That aligns with broader trends in tech hiring, where compensation packages are increasingly holistic, not just salary-based.
Compensation at the senior level is holding steady, especially for those with specialized experience in AI, fintech, or enterprise platforms, Harders says However, junior roles are seeing flattening salaries and more contract work. Some companies are expecting more for less, wanting a full-stack data scientist, but offering lower-tier pay.
