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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesData science can be a growing occupation and still be difficult to enter. Growth projections describe expected changes in total employment, not how many junior jobs are open near you, how many applicants compete for them, or what experience a particular employer expects. In the U.S., the Bureau of Labor Statistics (BLS) projects strong growth for data scientists—but its figures are not a promise of easy entry-level hiring.
Are data science jobs still in demand?
In the United States, the BLS projects data-scientist employment to grow 35% from 2025 to 2035. It estimates an average of about 24,800 openings per year over that period. Many openings are expected because workers transfer to other occupations or leave the labor force, so the annual total is not the number of newly created jobs. These are occupational projections, not a live count of vacancies or an entry-level hiring forecast. BLS Occupational Outlook Handbook: Data Scientists.
The figures apply to the U.S. occupation Data Scientists (SOC 15-2051). They do not automatically describe data analysts, business intelligence analysts, data engineers, or machine-learning engineers, whose work and hiring requirements can differ. Nor do national projections show whether a particular city or industry is hiring junior candidates.
Why can a growing field still be hard to enter?
Growth is not the same as accessible junior vacancies
A projected increase in total employment can coexist with few advertised entry-level roles. The BLS outlook covers an occupation over a decade; it does not report applicants per opening, the share of vacancies suitable for new graduates, or how many employers are willing to train. A candidate’s prospects depend on the actual jobs in their target location and role family.
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Employers may want several capabilities at once
Data-science work often combines quantitative reasoning, programming, data handling, and the ability to explain results in a business context. The BLS says a bachelor’s degree in mathematics, statistics, computer science, or a related field is typical for the U.S. occupation; some employers require or prefer graduate degrees. That profile describes typical expectations, not a rule every employer applies. The tools and depth required vary by job, so there is no single stack that fits every posting. BLS Occupational Outlook Handbook: Data Scientists.
A skills gap does not mean every applicant will pass screening
In its 2025 Future of Jobs Survey, the World Economic Forum reported that 63% of surveyed employers cited skills gaps as a primary barrier to business transformation. Employers also reported organizational culture or resistance (46%), regulatory concerns (39%), and insufficient data or technical infrastructure (32%). These are broad transformation barriers, not data-science recruiting statistics. They help explain why organizations can say they need skills while still seeking candidates with particular experience, education, domain knowledge, or communication ability. World Economic Forum, Future of Jobs Report 2025: Skills Outlook.
Early-career hiring may face additional pressure, but AI is not a proven explanation for data-science hiring difficulty
A 2026 U.S. Census Bureau working paper found a 12% regression-adjusted decline in early-career employment in the most AI-exposed industry-state quintile over the ten quarters after ChatGPT’s introduction. It also reports that hiring had largely recovered by early 2025 from a smaller employment base. This evidence covers broad industry-state groups, not data scientists specifically, and does not establish that AI caused a decline in entry-level data-science vacancies. U.S. Census Bureau working paper.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What skills do employers want?
There is no reliable universal checklist: requirements depend on the work. A reporting-focused role may emphasize SQL and visualization; a modeling position may call for stronger statistics and programming; a machine-learning engineering role may put more weight on deployment and software systems. Treat those as distinctions to verify in postings, not fixed rules for every employer.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11For a useful comparison, review current listings for the specific title family and location you want. Separate requirements from preferences, and note what the job actually involves:
- Work: reporting and dashboards, experimentation and product analysis, statistical modeling, machine-learning deployment, or data infrastructure.
- Skills: SQL, programming, statistics, visualization, cloud or platform tools, domain knowledge, and communication—only where the posting asks for them.
- Experience and education: whether a degree is required or preferred, whether internships or prior domain experience are expected, and whether the position is genuinely junior.
- Hiring context: geography, industry, employer size, and whether the role appears to be new headcount or replacement hiring, when that information is available.
Skill needs also change over time. The International Monetary Fund reports that roughly one in ten job postings in advanced economies required at least one new skill in its data, with IT skills accounting for more than half of new skills. The finding covers the wider labor market, not data-science postings alone. It supports keeping skills current, but it does not make “learn AI” a complete job-search plan. International Monetary Fund, How Artificial Intelligence Is Reshaping the Demand for Skills.
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How to make a more realistic job-search plan
- Choose a target role family and location. Compare data scientist, analyst, BI, data engineering, and machine-learning listings rather than treating “data” jobs as interchangeable.
- Read recent postings for evidence. Track recurring requirements and distinguish must-haves from preferences. Look for whether “entry level” roles still request prior experience.
- Build evidence around the work requested. Demonstrate relevant analysis, coding, data handling, and communication in a way that matches the role. A portfolio can show capability, but the cited evidence does not establish that any project, credential, course, or bootcamp guarantees a job.
- Use the national outlook as context, not a personal forecast. The BLS projection indicates expected U.S. occupational growth; it cannot tell you how quickly you will be hired or how many suitable openings exist in your area.
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