Indeed Data Shows Most Tech Hiring Collapsed — Data Scientists Were the Exception

Numerous job reports in the previous months conclude that overall tech hiring has clearly cooled. Indeed’s latest data supports this finding, revealing that tech job postings are down roughly 36% compared to the pre-pandemic boom. However, certain specialized roles haven’t collapsed in the same way. Data scientists, in particular, continue to show relative resilience—even amid layoffs, hiring freezes, and longer interview cycles.

For job seekers, this creates mixed signals: fewer openings overall, but persistent demand in specific niches. The reality is that as the quantity of tech roles shrinks, specialized skills, especially in data science, are increasingly acting as a differentiator, even in a down market. Broader labor analyses from outlets like TechTarget reinforce this trend, showing that tech hiring hasn’t disappeared, but has become far more selective.

According to Indeed, the 36% decline in tech job postings can be attributed to the fact that many companies overhired during the pandemic, then pulled back sharply as interest rates rose and growth expectations normalized. The result is what many analysts describe as a “low-hire, some-fire” mode. Organizations aren’t aggressively expanding headcount, but they also aren’t done reshaping teams.

Source: Indeed’s Best Jobs in the US for 2026 report

What’s notable is how data science compares within this slowdown. Based on Indeed’s aggregated job posting data, data scientist roles still account for meaningful demand. While wage growth has softened, postings growth remain positive, with a 15% growth over a 3-year period and a share of 958 per 1 million job postings.

This suggests demand hasn’t vanished and has instead stabilized. Titles may be shifting (analytics engineer, applied scientist, ML analyst), but employers continue to look for people who can work with data at a high level. And despite macro uncertainty still shaping cautious hiring across industries, data-oriented expertise remains a priority across industries.

Data science hasn’t dropped as sharply as many other tech roles because it is integral to multiple business-critical functions. Data scientists don’t just write code; they support analytics, machine learning, cloud pipelines, experimentation, and decision-making. That breadth makes the role harder to eliminate entirely.

TechTarget’s long-term outlook highlights this dynamic. Its analysis projects outsized growth for data scientists and data analysts over the next decade (often cited at over 400% projected growth), driven by the continued expansion of data-driven and AI-powered systems. Even if the exact number is debated, the direction is clear.

Demand also clusters around durable skills: Python, SQL, data visualization, statistics, and machine learning. These aren’t trendy tools that disappear overnight, they will always be in demand because they underpin AI initiatives and operational analytics alike.

This trend also aligns with a broader shift toward skills-first hiring. Employers prioritize measurable, applied capabilities over generic tech backgrounds, especially for remote or hybrid roles.

While this news presents a silver lining in an otherwise tight job market, none of this means data science is “easy” right now. Layoffs and slow growth have and continue to hit most tech subfields, including data and analytics. Automation has also reduced some roles, and companies are far more cautious about adding net-new headcount.

Candidates feel this acutely. Not only are entry-level roles shrinking, but applicant pools are also deeper, leading to competitive markets and hiring timelines that stretch on for months. Saturation is a valid concern raised frequently in tech communities.

What keeps data science relevant is its business impact. Data informs product direction, analytics supports AI deployment, and insights help companies do more with fewer resources. In a cost-conscious environment, those functions are harder to cut entirely.

For data scientists and aspiring data scientists, the takeaway is nuanced. If the market feels tougher, that’s not imagined. But it’s important to remember that the field hasn’t disappeared but continues to narrow. As covered in a previous article on AI and job demand, this means specialization matters more than ever.

Strong analytics foundations paired with applied AI or ML experience can meaningfully differentiate candidates, echoing the broader trend that generalists are being filtered out. Core skills still matter, too: Python, SQL, modeling, visualization, and increasingly, the ability to translate data into business outcomes. In downturns, roles tied to impact and not just output tend to persist.

Overall, the shrinking tech job market doesn’t signal the end of tech careers. It signals that employers are being far more deliberate about where they hire. Data science remains one of the areas where that hiring energy continues to concentrate—provided professionals adapt and position both their core and specialized skills accordingly.