Is Data, Analytics & AI a Good Job Market in Austin-Round Rock-San Marcos, TX?

Executive Verdict

Market rating: competitive | Confidence: High

Austin is still a workable market for Data, Analytics & AI, but it is not an easy one: metro unemployment was 3.9% in August 2026 and total nonfarm employment was up 1.1574% year over year, so the local economy is still adding jobs.[10][2] Within the local employer base, the Information sector was down 5.4902% year over year while Professional and Business Services was up 1.9587%, which points to softer pure-tech teams and steadier demand in consulting, services, and transformation work.[17][36] Texas-wide occupation data sharpens the picture: Data, Analytics & AI employment was down 1.3% year over year even as active postings were up 41.9%, a sign that employers are opening roles selectively rather than hiring broadly.[3][4]

Best positioned: Candidates with proven Python and SQL fluency plus a track record in machine learning, analytics delivery, or business-facing decision support have the best odds, especially in software, consulting, and public-sector teams.[26][24][9]

Main caution: The biggest mistake is reading AI buzz as broad junior demand: only about 15% of sampled roles were entry-level and only about 10% were remote.[24][8]

What Changed Recently

• Texas-wide Data, Analytics & AI openings were up 41.9% year over year in September 2026, while employment in the same occupation family was down 1.3%.[4][3]: That is a classic sign of a market with visible openings but tougher conversion into offers, especially for candidates without direct experience.
• Austin's Information supersector fell 5.4902% year over year in August 2026, while Professional and Business Services grew 1.9587%.[17][36]: You should broaden beyond pure tech employers and put more weight on consulting, services, and transformation teams.
• Recent Austin postings skew experienced and local: about 15% entry-level, about 40% mid-level, about 35% senior, and about 10% lead+, with about 50% on-site, about 40% hybrid, and about 10% remote.[24][8]: Job seekers limiting themselves to remote-only or true junior roles are competing for a small slice of the market.
• National unemployment was 4.2% in September 2026, CPI was up 3.0% year over year in August 2026, and the federal funds rate was 3.75%.[20][22][23]: The broader economy looks steady rather than hot, so Austin employers can still hire, but they are more likely to demand clear ROI and fewer ramp-up hires.

What This Means for You

Entry-Level Candidates

Difficulty: High.

Best target: Target bachelor's-level analyst roles in hybrid or on-site government, consulting, and enterprise teams rather than remote-first AI engineer titles; in the sample, bachelor's degrees were the most common stated requirement, while only about 15% of roles were entry-level and about 10% were remote.[35][24][8]

Biggest mistake: Applying only to 'data scientist' or 'AI engineer' titles without showing shipped analysis, dashboards, or reproducible SQL/Python work.

Next step: Build one portfolio project that combines SQL, Python, and a dashboard, and make it relevant to a real Austin-style use case such as public programs, SaaS metrics, or operations reporting.

Mid-Career Candidates

Difficulty: Moderate.

Best target: Aim at mid-level and senior analytics, decision support, and applied ML roles where Python and SQL are core and machine learning or AWS are differentiators; local postings most often ask for python (about 75%), sql (about 55%), machine learning (about 25%), and aws (about 15%).[9]

Biggest mistake: Presenting yourself as generic 'AI talent' instead of a person who has improved forecasting, automation, experimentation, or executive decision-making.

Next step: Rewrite your resume around business outcomes, not tools, and keep two versions ready: one for analytics/BI and one for applied AI or ML.

Career Switchers

Difficulty: High.

Best target: Your best entry is through domain-heavy analyst work in government, finance, operations, or program teams, then moving inward toward full data roles; the local market still centers on bachelor's-level hiring and only rarely requires formal certifications.[35][29]

Biggest mistake: Trying to compete head-on with experienced data scientists before you have proof that you can frame problems, clean data, and explain decisions.

Next step: Use your prior industry expertise as the hook, then add one credible skills signal such as a Power BI or Google Data Analytics credential plus a portfolio that shows business reasoning.[25]

Salary Reality

high pay highly concentrated

The cleanest local pay anchor is BLS wage data for Austin data scientists: $85,780 at the 25th percentile, $127,360 at the median, and $186,010 at the 90th percentile in 2025.[30][31][32] The local mean annual wage was $126,830.[1] Recent posted salary ranges from the Callings.ai job database center on about $121k to $178k, with a broader 25th-75th band of about $100k to $215k.[7] Statewide, Revelio Public Labor Statistics puts the mean offered salary on new Texas openings at about $123,361 in September 2026, based on n=3,863 postings with salary data.[33]

This is still a strong-paying market. Austin's BLS mean for data scientists was roughly in line with the national mean for the same occupation, $126,830 locally versus $126,800 nationally.[1][34]

The upside is offset by selectivity: only about 15% of sampled roles were entry-level, remote work accounted for about 10% of postings, and the market leans toward candidates who can already operate independently.[24][8]

Best-paying path: The strongest pay tends to sit in specialized senior work—applied ML, advanced analytics, and business-critical decision roles—where the senior-heavy mix and the upper end of current posted ranges are concentrated.[24][7][9]

Caution: Do not anchor on the top of the range alone: posted bands can be wide, and the highest salary figures usually reflect narrower, harder-to-win roles rather than the typical Austin opening.[7]

Where the Opportunities Are Concentrated

Real opportunity is concentrated in a few employer types, not spread evenly across every Austin company. In the recent local sample, software development accounted for about 35% of Data, Analytics & AI postings, while government administration, technology, consulting, and computer hardware development each contributed about 10%.[26] The most consistently active employers included TX-HHSC-DSHS-DFPS and Integra FEC at around 10 postings each, followed by Deloitte, Unchain Data, Bain & Company, TeamViewer, and News Corp at around 5 each.[6] That mix matters because Austin's broader Information sector was down 5.4902% year over year in August 2026, while Professional and Business Services was up 1.9587%.[17][36] In plain terms, waiting for a pure-tech moonshot role is riskier than pursuing analytics work inside consulting, public programs, enterprise operations, or product teams that need data talent tied to measurable business outcomes. Hiring is also fragmented across employers rather than dominated by one or two firms.[18] That lowers single-company risk, but it raises the importance of targeted applications: you will likely win by tailoring yourself to several submarkets, not by using one generic resume.

• Enterprise software and product analytics (high): This is the biggest local pocket, with software development representing about 35% of sampled postings.[26]
• Consulting and transformation teams (high): Consulting made up about 10% of the local posting mix, and active employers included Deloitte and Bain & Company.[26][6]
• Public-sector and regulated-program analytics (moderate): Government administration accounted for about 10% of sampled postings, with TX-HHSC-DSHS-DFPS among the most active named employers.[26][6]
• Hardware and tech-adjacent analytics (moderate): Computer hardware development contributed about 10% of the local mix, offering a useful alternative to consumer-software hiring cycles.[26]

Where to focus: Focus first on mid-level Python-plus-SQL roles inside software, consulting, and public-sector transformation teams, then branch into applied AI once you can prove delivery.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appeared in about 75% of sampled Austin postings, making it the clearest local baseline skill.[9]
• SQL (table stakes): SQL showed up in about 55% of sampled postings, so weak SQL will block more interviews than weak modeling depth.[9]
• Machine learning (differentiator): Machine learning appeared in about 25% of local postings, which means it is not universal but does separate general analytics candidates from higher-bar applied-AI candidates.[9]
• Tableau and data visualization (differentiator): Tableau, data analysis, and data visualization each appeared in about 15% of local postings, showing that insight communication still matters in Austin.[9]
• AWS and cloud data fluency (premium): AWS appeared in about 15% of local postings, but AWS certification was explicitly required in less than 5%, so practical cloud fluency matters more than the badge alone.[9][29]
• AI output validation and prompt use (differentiator): Prompt engineering is becoming a valuable productivity skill, and the ability to question and validate AI-generated results is emerging as a critical advantage for data professionals.[27][28]
• Statistics and experimental reasoning (premium): Modern data-science work still rests on foundations in statistics, machine learning, data visualization, Python, and SQL.[27]
• Google Data Analytics Professional Certificate or Microsoft Power BI Data Analyst Associate (differentiator): These are among the more visible analytics certifications in 2026, and they are most useful as proof-of-skill signals in a market where explicit certification requirements are rare.[25][29]

Adjacent Roles to Consider

• Business Systems Analyst (bridge): It uses the same analytical thinking, requirements translation, and data-to-decision skills, but puts more weight on process and systems than on modeling.
• Financial or Risk Analyst (both): This path rewards SQL, reporting, forecasting, and anomaly detection, especially if you already understand finance, operations, or compliance.
• Revenue Operations Analyst (pivot): It is a good pivot for candidates strong in dashboards, pipeline analysis, experimentation, and business storytelling.
• Program Analyst (bridge): This is a practical bridge into government, education, and operations-heavy employers that still need data interpretation and reporting discipline.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two versions: one for analytics/BI and one for applied AI or ML, and put Python, SQL, business impact, and stakeholder outcomes above tool lists.[9]
• Build one portfolio piece that includes SQL extraction, Python analysis, and a dashboard, because Austin demand still strongly rewards that stack.[9]
• Broaden your search to hybrid and on-site roles instead of remote-only; only about 10% of sampled postings were remote.[8]
• Create a target list around the employers and segments that actually show up locally, including TX-HHSC-DSHS-DFPS, Integra FEC, Deloitte, Bain & Company, TeamViewer, News Corp, and Unchain Data.[6]
• If you are a switcher or recent graduate, add one light credential only if it comes with a project you can show in interviews.[25]

Days 31-60

• Publish two case studies: one descriptive analytics project and one forecasting, experimentation, or automation project that shows judgment rather than just code.
• Start applying by segment, not by title: one application set for software/product analytics, one for consulting, and one for public-sector or regulated-program roles.[26]
• Practice explaining where you used AI tools and where you overrode or validated them, since employers increasingly value that judgment layer.[27][28]
• Add one cloud or deployment-flavored artifact if you want better odds on premium roles, because AWS appears often enough to matter even if the certification itself is rarely required.[9][29]

Days 61-90

• If interviews are not converting, pivot some of your pipeline into adjacent roles such as business systems analyst, revenue operations analyst, or program analyst instead of waiting for ideal data-scientist titles.
• Choose a domain and commit to it for the next quarter: public programs, SaaS/product analytics, finance, or operations. Austin is not rewarding generic positioning right now.
• Use every final-round process to collect evidence on whether your weak spot is SQL depth, business framing, experimentation, dashboarding, or production readiness, then fix only that bottleneck.
• If you are entry-level, prioritize paid contract, fellowship, or internal-transfer style paths over holding out for a remote junior AI title.

Methodology and Confidence

This September 2026 report was generated on October 10, 2026. Latest direct national data: September 2026. Latest direct Austin-Round Rock-San Marcos, TX data: September 2026.

Confidence: Overall confidence: High. Recent local occupation, wage, and market-context data are available, but some role-specific and posting-based signals are still partial.

Limitations

• The best local wage and occupation anchors here come from BLS estimates published in August 2026 for May 2025, so they are reliable benchmarks but not a live read on September 2026 hiring conditions.[1]
• Data scientist is the strongest directly measured Austin title in the official wage data, but this category is broader and also includes roles such as BI analysts, analytics engineers, ML engineers, AI engineers, statisticians, and decision scientists, so some sub-role conclusions are approximate rather than separately counted.[1]
• Statewide labor data was used as a proxy where metro-level Revelio Public Labor Statistics is not published, which is why some September 2026 direction signals refer to Texas rather than Austin alone.[3][4]
• The Callings.ai job database is a partial, deduplicated sample of online postings, so leading employer names, skill patterns, salary bands, and work-arrangement mix are more reliable than exact counts or exact market shares.[5][6][7][8][9]
• Some August 2026 labor-force and unemployment changes are preliminary government estimates and can be revised in later releases.[10][11][12][13][14]

References

1. Bureau of Labor Statistics. Occupational Employment and Wages in Austin-Round Rock-San Marcos — May 2025 : U.S. Bureau of Labor Statistics · 2026-08 · bls.gov
2. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
3. Reveliolabs. Employment — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
4. Reveliolabs. Job Openings — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
5. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
6. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
7. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
8. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
9. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
10. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
11. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
12. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
13. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
14. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
15. Reveliolabs. Mass-layoff Notices — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
16. Statesman. Client Challenge · 2026-09 · statesman.com
17. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
18. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
19. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
20. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
21. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
22. Federal Reserve Economic Data. Consumer Price Index for All Urban Consumers: All Items in U.S. City Average · 2026-08 · fred.stlouisfed.org
23. Federal Reserve Economic Data. Federal Funds Effective Rate · 2026-09 · fred.stlouisfed.org
24. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
25. Ucertify. Top Data Analytics Certifications to Advance Your Career in 2026 · 2026-06 · ucertify.com
26. Askmetraining. You are being redirected... · 2026-09 · askmetraining.in
27. Workforceinstitute. AI Data Analyst Role in 2026 – Career Guide for Students | WorkForce Institute · 2026-02 · workforceinstitute.io
28. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
29. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
30. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
31. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
32. Reveliolabs. Salaries — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
33. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
34. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
35. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov

Live Openings in This Market

This report is published monthly; its companion page tracks the active Data, Analytics & AI openings in Austin-Round Rock-San Marcos, TX from the live Callings.ai job index. Browse current Data, Analytics & AI openings in Austin-Round Rock-San Marcos, TX.