Introduction: The Data Market Decoded The tech landscape of 2025 is no longer just about “getting into data.” It has become a complex ecosystem of specialized niches, varying competition levels, and distinct geographical demands. Using a specialized dataset of 3,213 LinkedIn job postings for Data Analysis, Data Science, ML Development, and Data Engineering, we’ve performed a rigorous analysis to see where the market is headed.

Whether you are a job seeker looking for the path of least resistance or a hiring manager benchmarking your roles, these insights reveal the true pulse of the industry. By moving beyond anecdotal evidence and using statistical modeling, we can identify exactly where the “crowded rooms” are—and where the “open doors” might be hidden.

Research Questions

This analysis is anchored by three primary inquiries into the current market:

  1. The Geography of Demand: How do job demand patterns (number of postings and applicants) vary by location and searched country for data-related roles?

  2. Seniority vs. Competition: What relationships exist between seniority level, employment type, and applicant volume?

  3. The Industry Influence: How do specific industries and job functions influence the prevalence and competitiveness of data-related positions?

Question 1: Jobs and Location—Where is the Demand?

The first step in our audit is understanding “The Where.” Job volume is a measure of opportunity, but “Applicant Intensity” (applicants divided by days posted) is our measure of competition. High volume doesn’t always mean a high chance of success if the intensity is equally high.

Top 10 Locations by Volume

The following table highlights the metropolitan hubs currently driving the data economy.

Table 1 Top 10 Locations by LinkedIn Job Postings
Location Total Jobs Posted Total Applicants Average Applicant Intensity
United States 328 38050 93.43
New York, NY 142 15975 101.53
Madrid, Community of Madrid, Spain 100 6450 53.03
San Francisco, CA 94 11025 81.36
Paris, Île-de-France, France 60 3750 78.19
Warsaw, Mazowieckie, Poland 59 2250 16.35
Barcelona, Catalonia, Spain 51 3325 54.69
Milan, Lombardy, Italy 46 2650 36.09
Dublin, County Dublin, Ireland 44 5675 41.77
Amsterdam, North Holland, Netherlands 43 1850 16.56
a Note. Total postings, total applicants, and average applicant intensity for each location. Data collected from LinkedIn.

Interpretation: We see a clear concentration of roles in major tech capitals. However, look closely at the Average Applicant Intensity. In many cases, “secondary” cities might have lower total postings but also significantly lower intensity, making them prime targets for a more efficient job search.

Visualizing the Competition

While the tables give us the exact numbers, the visualizations below help us grasp the scale of the difference between the most active hubs and the most competitive ones.

Interpretation: The chart above shows that job volume is highly top-heavy. The “Big Players” dominate the market, but as we see in the intensity chart below, being in a high-volume city comes at a cost: extreme competition.

Question 2: Seniority and Employment Type—Is Experience the Shield?

A common theory is that “Senior” roles are less competitive than “Entry-level” roles. We tested this by crossing Seniority Level with Employment Type to see where the applicants are actually flocking.

Table 4: Top 10 Seniority Level × Employment Type by Total Postings
Seniority Level Employment Type Total Postings Total Applicants Avg Applicant Intensity
Mid-Senior Level Full-Time 946 64525 69.04746
Entry Level Full-Time 930 80300 74.58007
Not Applicable Full-Time 392 32775 59.84848
Associate Full-Time 340 31850 78.52466
Mid-Senior Level Contract 196 19650 97.68698
Internship Internship 81 5475 85.38435
Entry Level Contract 54 5575 98.63946
Executive Part-Time 49 625 13.83333
Internship Full-Time 44 5200 63.18182
Associate Contract 40 3900 110.27569

Stacked Bar: Analyzing the Structure of Opportunities

This visualization helps us understand if the market is favoring permanent full-time roles versus contract or internship positions.

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## the `.groups` argument.

Interpretation: The vast majority of the data market remains focused on Full-time roles, specifically at the Mid-Senior and Entry levels. Interestingly, the high volume of Entry-level roles doesn’t necessarily mean they are “easier” to get—it often means they attract a disproportionate number of applicants.

Statistical Validation: Kruskal-Wallis Analysis

To move beyond visual trends, we performed a Kruskal-Wallis test to see if the differences in applicant volume across seniority and employment types were statistically significant.

Table X: Kruskal-Wallis Test for Applicants by Seniority Level
Test Variable H df p
Kruskal-Wallis chi-squared Kruskal-Wallis Applicants by Seniority Level 88.11 6 < .001

The Verdict: With a p-value < .001, the difference is real. The competition intensity is fundamentally different for a Senior role compared to an Entry role. If you are struggling at the entry-level, the statistics show it’s not just “you”—the statistical intensity of the competition is significantly higher in that category.

Question 3: Industries and Job Functions—The “Safe” Zones

Which sectors are hiring, and which are over-saturated? By examining the intersection of Industry and Job Function, we can find the “Sweet Spots.”

Table 5: Top 10 Industries × Job Function by Total Postings
Industry Job Function Total Postings Total Applicants Avg Applicant Intensity
IT Services and IT Consulting Information Technology 275 20550 67.71603
Software Development Engineering and Information Technology 129 12100 77.92228
Software Development Information Technology 117 9900 57.78450
IT Services and IT Consulting Other 113 3425 19.21981
IT Services and IT Consulting Engineering and Information Technology 92 4300 37.14442
Technology, Information and Internet Information Technology 83 8300 85.37234
Financial Services Information Technology 64 6100 70.47619
Technology, Information and Internet Engineering and Information Technology 64 5225 92.29950
Staffing and Recruiting Engineering and Information Technology 54 2550 157.14286
Wholesale Sales 48 600 14.06250

The Competition by Role

Not all data roles are created equal. The table below shows the average intensity for different job functions.

Average Applicant Intensity by Job Function
job_function avg_applicant_intensity
Administrative and Analyst 200.00000
Administrative, Research, and General Business 200.00000
Analyst, Finance, and Administrative 200.00000
Analyst, Information Technology, and Strategy/Planning 200.00000
Analyst, Research, and Other 200.00000
Business Development, Analyst, and Finance 200.00000
Consulting, Research, and Analyst 200.00000
Engineering, Research, and Information Technology 200.00000
Finance, Engineering, and Information Technology 200.00000
General Business, Customer Service, and Accounting/Auditing 200.00000
Information Technology, Analyst, and Business Development 200.00000
Information Technology, Analyst, and General Business 200.00000
Information Technology, Consulting, and Business Development 200.00000
Information Technology, Engineering, and Other 200.00000
Management 200.00000
Product Management, Information Technology, and Management 200.00000
Project Management 200.00000
Project Management and Information Technology 200.00000
Engineering and Analyst 166.66667
Analyst, Finance, and Strategy/Planning 150.00000
Strategy/Planning 150.00000
Quality Assurance, Information Technology, and Analyst 142.85714
Design 138.09524
Education and Training 133.33333
Marketing 123.80952
Business Development 123.46939
Business Development and Sales 120.00000
Consulting, Information Technology, and Sales 120.00000
Management and Strategy/Planning 120.00000
Research, Analyst, and Information Technology 116.07143
Analyst and Information Technology 111.11111
Finance and Sales 106.25000
Engineering and Research 104.76190
Analyst and Finance 100.00000
Analyst and Marketing 100.00000
Analyst and Other 100.00000
Analyst and Product Management 100.00000
Analyst, Business Development, and Consulting 100.00000
Analyst, Business Development, and Project Management 100.00000
Analyst, Customer Service, and Consulting 100.00000
Analyst, General Business, and Information Technology 100.00000
Analyst, Information Technology, and Other 100.00000
Analyst, Information Technology, and Product Management 100.00000
Analyst, Manufacturing, and Information Technology 100.00000
Analyst, Other, and Information Technology 100.00000
Analyst, Sales, and Customer Service 100.00000
Analyst, Strategy/Planning, and Information Technology 100.00000
Analyst, Strategy/Planning, and Management 100.00000
Business Development, Analyst, and Accounting/Auditing 100.00000
Consulting and Information Technology 100.00000
Engineering, Analyst, and Information Technology 100.00000
Engineering, Business Development, and General Business 100.00000
Engineering, Other, and Information Technology 100.00000
Engineering, Science, and Project Management 100.00000
Finance, Analyst, and Strategy/Planning 100.00000
Human Resources 100.00000
Information Technology and Business Development 100.00000
Information Technology, Engineering, and Analyst 100.00000
Marketing and Sales 100.00000
Research, Engineering, and Information Technology 100.00000
Science and Research 100.00000
Analyst 96.22751
Research 92.55952
Finance 92.02381
Analyst, Finance, and Information Technology 88.88889
Engineering 78.81752
General Business, Strategy/Planning, and Consulting 78.12500
Engineering and Information Technology 75.08216
Information Technology 74.99910
Consulting 72.13141
Information Technology and Analyst 68.88889
Analyst and Consulting 66.66667
Business Development and Information Technology 66.66667
Finance, Analyst, and Information Technology 66.66667
Information Technology, Analyst, and Customer Service 66.66667
Other, Information Technology, and Engineering 66.66667
Analyst and Engineering 64.28571
Information Technology and Engineering 63.43951
Engineering, Information Technology, and Analyst 59.33333
Science 57.20238
Consulting and Finance 56.19048
Analyst and Distribution 50.00000
Analyst, Consulting, and Supply Chain 50.00000
Consulting, Analyst, and Science 50.00000
Consulting, Engineering, and Information Technology 50.00000
Finance and Engineering 50.00000
General Business and Administrative 50.00000
Research and Information Technology 50.00000
Science and Engineering 50.00000
Analyst and Strategy/Planning 40.00000
Engineering, Analyst, and Finance 40.00000
Engineering, Information Technology, and Management 40.00000
Engineering, Information Technology, and Strategy/Planning 40.00000
Information Technology, Science, and Analyst 40.00000
Other and Information Technology 40.00000
Strategy/Planning and Information Technology 39.68750
Management and Manufacturing 36.70635
Other 36.34815
Quality Assurance 33.88889
Analyst, Consulting, and Engineering 33.33333
Analyst, Consulting, and Information Technology 33.33333
Analyst, Engineering, and Information Technology 33.33333
Business Development, Information Technology, and Administrative 33.33333
Information Technology, Analyst, and Other 33.33333
Information Technology, Product Management, and Finance 33.33333
Project Management, Information Technology, and Engineering 33.33333
Health Care Provider 32.77778
Sales, Advertising, and Marketing 28.57143
Accounting/Auditing and Finance 28.12500
Administrative 25.55691
Information Technology and Consulting 25.39683
Engineering, Information Technology, and Business Development 25.00000
Engineering, Information Technology, and Other 25.00000
Information Technology and Manufacturing 25.00000
Information Technology, Consulting, and Engineering 25.00000
Other, Information Technology, and Management 25.00000
Production and Manufacturing 25.00000
Sales and Business Development 25.00000
Sales 23.66071
Customer Service 21.42857
General Business 13.96429
Research and Science 12.50000
Accounting/Auditing 9.52381
Finance and Consulting 9.52381
Education 6.25000

Final Statistical Check: Industry and Function Significance

Finally, we run our non-parametric tests on Industry and Job Function.

Table X: Kruskal-Wallis Test for Applicant Intensity by Job Function
Test Variable H df p
Kruskal-Wallis chi-squared Kruskal-Wallis Applicant Intensity by Job Function 261.28 124 < .001

What this means for your career: The high H-statistic (261.28) for Job Function confirms that what you call yourself matters. In 2025, the market treats “Data Scientists” and “Data Engineers” as two completely different competitive ecosystems. Applying to the right title is the single most effective way to lower your competition.

Final Takeaways: How to Win in 2026

  1. Escape the High-Intensity Hubs: If you are remote-capable, target cities with lower applicant intensity rather than fighting the crowds in New York or London.

  2. Specialization is Your Shield: The Kruskal-Wallis results prove that generic roles are oversaturated. Transitioning from “Data Analyst” to a specialized function like “ML Development” or “Data Engineering” moves you into a statistically less competitive bracket.

  3. Seniority Matters, but so does Sector: Don’t just look for “Senior” roles; look for roles in industries where the applicant intensity is lower. The “Safety Zones” are often in traditional industries (like Manufacturing or Finance) rather than pure Tech.

The 2025 data market didnt shrinking—it’s just got smarter. To succeed, you have to be just as data-driven as the roles you’re applying for.