Is Data, Analytics & AI a Good Job Market in San Antonio-New Braunfels, TX?

Executive Verdict

Market rating: competitive | Confidence: High

San Antonio is a workable but selective market for Data, Analytics & AI right now. Local labor conditions are mixed: metro unemployment was 4.5% in August 2026, total nonfarm employment was up only 0.5145% year over year, professional and business services grew 4.7742%, and the information sector fell 7.6923%.[1][2][3][4] For this category, the strongest directional signal is that Texas data, analytics & AI postings were up 41.9% year over year even as state employment in the field was down 1.3%, which points to active search activity but careful hiring.[5][6] Local openings also skew mid-career and hybrid, with about 15% entry-level roles and about 10% remote in the observed sample.[7][8]

Best positioned: A mid-career analyst or data scientist who can show Python, SQL, and business-facing delivery—and is open to hybrid roles at firms like Booz Allen, USAA, Ernst & Young LLP, or Tata Consultancy Services Limited—has the best odds right now.[9][10]

Main caution: Nationally, BLS still projects 33.5% growth in data scientist employment through 2034, but the visible local market was only more than 30 postings across more than 20 companies over the last 90 days, so landing speed can still be slow.[11][12]

What Changed Recently

• San Antonio payrolls edged up at a 0.7% annualized rate from May through August 2026, but payrolls were still down 0.4% year to date, while the Dallas Fed said the metro unemployment rate fell to 4.2% in August.[36]: The market is not collapsing, but it is not broad-based either, so hiring managers can stay selective.
• The local sector backdrop split further: San Antonio information employment was 16.8 thousand in August and down 7.6923% year over year, while professional and business services was 162.4 thousand and up 4.7742%.[4][3]: That pushes the best local odds toward analytics embedded in consulting, operations, finance, and client services rather than pure tech employers.
• At the Texas occupation level, Data, Analytics & AI employment was down 1.3% year over year in September 2026, but active postings were up 41.9%.[6][5]: More roles are being advertised, but employers may be reopening searches, widening requirements, or hiring more cautiously than posting volume alone suggests.
• National inflation was up 3.0% year over year in August, average hourly earnings were up 3.0% in September, and the federal funds rate was 3.75%.[33][35][34]: Real wage gains are modest and financing conditions are still restrictive, so employers tend to reward clearly job-ready skill stacks more than broad 'AI interest.'
• National research found entry-level data scientist hiring weakened from 2022 to 2025 while senior-level hiring stayed stronger, and the local sample also shows only about 15% entry-level openings.[37][7]: Early-career candidates should aim first at analyst, BI, or applied analytics lanes rather than holding out only for AI-branded titles.

What This Means for You

Entry-Level Candidates

Difficulty: High, because the observed local mix is only about 15% entry-level and most postings that state education requirements still center on a bachelor's degree rather than a training-only path.[7][30]

Best target: Target analyst and BI-style roles where you can prove SQL, Power BI, data visualization, and some Python on business datasets, instead of leading with 'AI engineer.'[10]

Biggest mistake: Applying to machine-learning-heavy roles without a portfolio, cloud signal, or domain story when machine learning appears in about 40% of postings but entry share is still small.[10][7]

Next step: Build one portfolio project around a local-style business problem—retail inventory, claims/risk, or mission operations—and make the README explain the business decision, not just the model.[21]

Mid-Career Candidates

Difficulty: Moderate: the market is selective, but it is built more for experienced candidates because about 60% of observed openings were mid-level and about 30% were senior.[7]

Best target: Aim at hybrid roles in consulting, insurance, and defense-adjacent employers such as Booz Allen, USAA, Ernst & Young LLP, Tata Consultancy Services Limited, and Vista Innovative Solutions, LLC.[9][8]

Biggest mistake: Over-filtering for remote work; only about 10% of observed local openings were remote, versus about 50% hybrid and about 40% on-site.[8]

Next step: Rewrite your resume around shipped outcomes in Python, SQL, machine learning, and stakeholder-facing analytics, and add one cloud or ML credential if your background looks tool-stale.[10][22]

Career Switchers

Difficulty: High unless you can carry in a strong domain story from finance, operations, healthcare, public sector, or retail.

Best target: Use your prior industry as the wedge and pursue reporting, operations analytics, risk analytics, or customer insights work where business context matters as much as modeling depth.

Biggest mistake: Branding yourself as a generic AI generalist instead of a domain expert who now uses SQL, Python, dashboards, and structured analysis to solve a known business problem.[10]

Next step: Translate your old work into three analytics case studies with measurable decisions, then pursue a bachelor's-aligned analyst role first and treat advanced AI titles as a second move.[30]

Salary Reality

high pay highly concentrated

Observed government pay is solid but not elite. BLS puts San Antonio data scientists at a $103,290 median, with $78,000 at the 25th percentile and $134,360 at the 75th percentile in annual 2025 data.[13][14][15] A narrower specialty, computer and information research scientists, shows a $94,220 median and $107,940 mean locally.[17][16] By contrast, the current local posting sample centers on about $90k to $133k, and Revelio Public Labor Statistics shows mean offered pay on new Texas data, analytics & AI openings around $123,361 in September 2026 (n=3,863).[23][24]

San Antonio can support six-figure analytics careers, especially once you are past entry level, but the market usually prices below the national data scientist median of $120,230.[14][25]

The pay upside is offset by a thin local opening count, a mid-career skew, and limited remote flexibility.[12][7][8]

Best-paying path: The strongest ceiling likely sits in senior machine-learning, research-heavy, or cloud-centered roles, especially when paired with AWS, Google Cloud, Azure, Databricks, or defense-related compliance signals.[22]

Caution: Do not overread the local 90th-percentile figures of $165,250 and $170,850; those come from specific occupation codes, while the broader local posting band runs from about $68k to $222k because it mixes titles, employers, and seniority levels.[26][27][23]

Where the Opportunities Are Concentrated

Real opportunity looks concentrated in business-facing analytics rather than pure product-tech teams. San Antonio's information sector was 16.8 thousand jobs in August 2026 and down 7.6923% year over year, while professional and business services reached 162.4 thousand and grew 4.7742%.[4][3] In the local hiring sample, the most consistently active names were Tata Consultancy Services Limited, Vista Innovative Solutions, LLC, Booz Allen, USAA, and Ernst & Young LLP.[9] That mix points to three practical lanes. First is consulting and contractor work, where employers need Python, SQL, BI, and client-facing problem solving that can move across projects.[9][10] Second is finance, insurance, and risk-adjacent analytics, supported by USAA's presence and Texas's large financial activities base even though state financial employment was down slightly year over year.[9][28] Third is applied analytics tied to operations, retail, and public missions: UT San Antonio highlighted local work with H-E-B and the U.S. Army Forensic Toxicology Program at Fort Sam Houston, including computer-vision shelf analysis and workforce-data analysis.[21] What this evidence does not show is a deep, remote-first consumer-AI employer base in San Antonio. Only about 10% of observed local openings were remote, and the sample itself was just more than 30 postings across more than 20 companies over 90 days.[12][8]

• Consulting and contractor analytics (high): The clearest local employer cluster runs through firms such as Booz Allen, Tata Consultancy Services Limited, Ernst & Young LLP, and Vista Innovative Solutions, LLC, which favors candidates who can translate data work into client or mission outcomes.[9]
• Finance, insurance, and risk analytics (moderate): USAA's presence makes this a strong lane for candidates who pair analytics with fraud, risk, controls, pricing, or operations experience.[9]
• Retail, operations, and public-mission analytics (moderate): Local applied work with H-E-B and Army-affiliated programs suggests durable demand for inventory, geospatial, operations, and decision-support analytics even outside headline AI roles.[21]
• Pure info-sector tech employers (limited): The local information sector is shrinking year over year, so candidates relying only on startup-style or media/platform employers have a narrower path.[4]

Where to focus: Prioritize hybrid analytics roles embedded in consulting, finance-insurance, and mission operations, and treat pure remote AI-role searches as a side bet.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appears in about 65% of observed local postings, making it the baseline language for both analyst and data-science tracks.[10]
• SQL (table stakes): SQL shows up in about 60% of local postings, which means data access and query fluency still matter more than AI branding.[10]
• Power BI and data visualization (differentiator): Power BI and data visualization each appear in about 25% of observed postings, which makes them a practical edge for roles closer to reporting, business decision support, and stakeholder storytelling.[10]
• Machine learning and statistical modeling (premium): Machine learning appears in about 40% of local postings and statistical modeling in about 20%, so advanced modeling is valuable but not universal across the whole category.[10]
• AWS and Snowflake (differentiator): AWS and Snowflake each appear in about 20% of observed postings, which signals demand for candidates who can work inside modern cloud data stacks rather than only notebook workflows.[10]
• Cloud AI / ML certifications (differentiator): The most frequently cited certifications locally include AWS Certified Machine Learning Engineer - Associate, Databricks Machine Learning Associate, AWS Certified AI Practitioner, Google Cloud Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate, and NVIDIA AI/Deep Learning certification, each at about 5% of observed postings.[22]
• DoD 8140 baseline level II certification (premium): This certification appears among the most common local requirements, which is unusual and points to defense-adjacent demand in the San Antonio mix.[22]

Adjacent Roles to Consider

• Market research analyst (bridge): It uses similar SQL, dashboarding, experimentation, and insight-writing habits, but sits closer to marketing and customer insights than core data-science hiring.
• Supply chain analyst (both): Local applied work around H-E-B shelf images, inventory insights, and store comparison suggests a practical route into analytics that lives inside operations rather than a central data team.[21]
• Risk or fraud analyst (both): USAA and other business-facing employers make this a realistic path for candidates who can combine analytics with controls, fraud, underwriting, or claims logic.[9]
• Program analyst or mission analyst (pivot): Booz Allen, Vista, and Army-linked local project work suggest a lane where decision support and operational analysis matter more than cutting-edge model research.[9][21]

30 / 60 / 90-Day Plan

First 30 Days

• Build two resume versions: one for analyst/BI roles built around SQL, Power BI, and data visualization, and one for advanced roles built around Python, machine learning, statistical modeling, and cloud tools.[10]
• Create one portfolio project tied to a local-style use case such as retail inventory optimization, fraud/risk analysis, or mission operations support, and write the business recommendation as clearly as the code.[21]
• Stop screening only for remote work and widen to hybrid roles, because the observed local split is about 50% hybrid and about 40% on-site.[8]
• Make a target list around the named employer mix first—Booz Allen, USAA, Ernst & Young LLP, Tata Consultancy Services Limited, and Vista Innovative Solutions, LLC—then add similar firms in consulting and contractor ecosystems.[9]

Days 31-60

• Add one market-visible credential from the local pattern: AWS ML, Google Cloud ML, Azure AI Engineer, Databricks ML, or DoD 8140 if you want defense-adjacent work.[22]
• Rework your LinkedIn and resume bullets so every project shows business impact, stakeholder audience, and tools used; this market rewards applied analytics more than abstract AI positioning.
• For each application, map your experience to one of the strongest local lanes: consulting/client delivery, finance-insurance risk, or mission operations.
• Use alumni, UT San Antonio, and local tech-community touchpoints to get project feedback and referrals instead of only cold-applying.

Days 61-90

• If core data-science traction is weak, expand deliberately into adjacent roles such as supply chain analyst, risk analyst, market research analyst, or program analyst rather than waiting for perfect-title matches.
• Refresh your portfolio with one stakeholder-facing deliverable—a dashboard, memo, or decision deck—because the strongest local paths are business-embedded, not purely research-oriented.
• Use compensation conversations to anchor on role scope and tool ownership, not just title, since observed local pay is solid but concentrated and mixed across sub-roles.[23][14]
• If you are still missing interviews, narrow your search to one domain and one value story so recruiters can place you quickly inside a known business problem.

Methodology and Confidence

This September 2026 report was generated on October 10, 2026. Latest direct national data: September 2026. Latest direct San Antonio-New Braunfels, TX data: September 2026.

Confidence: Overall confidence: High. Recent local labor data, occupation-specific pay benchmarks, and current employer-composition signals point in the same broad direction.

Limitations

• Some of the best local pay benchmarks for this category are annual 2025 occupation estimates, so they are useful for setting salary expectations but not for tracking month-to-month changes in 2026.[13][14][15][16][17]
• This category is broader than any single title, and local government data does not cleanly publish every sub-role in one place, so figures for data scientists or research scientists are anchors rather than a full census of all analytics and AI work in San Antonio.[13][14][16][17]
• Statewide occupation data was used as a proxy for some demand direction because comparable monthly metro-level occupation hiring data is not published in the same series for San Antonio.[6][5]
• The August 2026 metro unemployment and employment readings are current but can still be revised, so small year-over-year changes should be read as directional rather than final.[1][18][19][20]
• The Callings.ai job database is a partial, deduplicated sample of online postings, so it is more reliable for leading employer names, work-arrangement mix, seniority mix, and skill patterns than for exact local counts or precise market share.[12][9][8][7][10]

References

1. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
2. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
3. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
4. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
5. Reveliolabs. Job Openings — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
6. Reveliolabs. Employment — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
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. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
11. Bureau of Labor Statistics. Artificial intelligence, information technology, and employment, 2024–34 : The Economics Daily : U.S. Bureau of Labor Statistics · 2026-07 · bls.gov
12. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
13. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
14. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
15. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
16. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
17. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
18. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
19. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
20. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
21. News. Solving real-world challenges with data, AI and machine learning - UT San Antonio Today · 2026-09 · news.utsa.edu
22. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
23. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
24. Reveliolabs. Salaries — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
25. Bureau of Labor Statistics. Data Scientists · 2026-08 · bls.gov
26. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
27. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
28. Federal Reserve Economic Data. All Employees: Financial Activities in Texas · 2026-08 · fred.stlouisfed.org
29. Reveliolabs. Mass-layoff Notices — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
30. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
31. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
32. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
33. Federal Reserve Economic Data. Consumer Price Index for All Urban Consumers: All Items in U.S. City Average · 2026-08 · fred.stlouisfed.org
34. Federal Reserve Economic Data. Federal Funds Effective Rate · 2026-09 · fred.stlouisfed.org
35. Federal Reserve Economic Data. Average Hourly Earnings of All Employees, Total Private · 2026-09 · fred.stlouisfed.org
36. Dallasfed. San Antonio Economic Indicators, August 2026 · 2026-09 · dallasfed.org
37. Deloitte. AI is changing work. It might also be limiting how expertise is built · 2026-06 · deloitte.com

Live Openings in This Market

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