Is Data, Analytics & AI a Good Job Market in Salt Lake City-Murray, UT?

Executive Verdict

Market rating: competitive | Confidence: High

Salt Lake City-Murray is still a viable market for Data, Analytics & AI, but it is not an easy one. Metro unemployment was 3.7% in August 2026, total nonfarm employment was up 2.4869% year-over-year, and professional and business services employment was up 4.3098%, which supports hiring for analytics roles tied to operations, finance, and service functions.[33][34][9] At the same time, local information employment was down -6.6667% year-over-year, Utah-wide Data, Analytics & AI employment was down 2.1% year-over-year even as postings were up 13.6%, and the recent local posting mix leaned mid-career and senior.[10][12][11][13]

Best positioned: The best odds right now go to mid-career candidates who can show SQL, Python, and dashboard or ML work tied to measurable business outcomes, especially in business services or finance-adjacent settings.[26][9][27][13]

Main caution: Do not mistake strong salaries for easy access: the recent local sample showed more than 30 postings across more than 20 companies over 90 days, with about 0% remote and only about 20% entry-level roles.[22][14][13]

What Changed Recently

• Salt Lake City-Murray professional and business services employment reached 154.9 thousand in August 2026 and was up 4.3098% year-over-year, while local information employment fell to 22.4 thousand and was down -6.6667%.[9][10]: That shifts the best odds toward analytics work embedded in service, operations, and finance teams rather than pure tech-product employers.
• Revelio Public Labor Statistics shows Utah Data, Analytics & AI postings up 13.6% year-over-year in September 2026 even as statewide employment in the field was down 2.1% year-over-year.[11][12]: Openings exist, but many appear to be selective fills or replacement hiring rather than broad team expansion.
• BLS still shows Salt Lake City-Murray as unusually concentrated for data scientists, with about 2,970 local data scientist jobs and a location quotient of 2.13 in May 2025.[29][21]: This metro is worth targeting if your profile matches real analytics or data science work, not just generic tech titles.
• National conditions are steady rather than hot: U.S. unemployment was 4.2% in September 2026, total nonfarm payrolls were up 0.3128% year-over-year, and average hourly earnings were up 3.0% year-over-year.[4][5][7]: That usually supports continued hiring for must-have analytics roles, but not the kind of loose market where employers lower their bar quickly.
• A regional risk signal appeared late in the month when Domo laid off 175 workers at its American Fork headquarters on September 23, one day after Progress Software completed a $400 million acquisition of Domo’s AI and data business.[2]: Local AI and analytics demand can still be disrupted by restructurings and acquisition-driven team changes.

What This Means for You

Entry-Level Candidates

Difficulty: High, because only about 20% of the recent local sample was entry-level and about 50% was mid-level.[13]

Best target: Target analyst and BI roles where you can show SQL, Python, Tableau, and Excel together, because those were the most common skills in the recent local mix.[26]

Biggest mistake: Applying as "open to anything in AI" without a portfolio that proves data cleaning, dashboarding, and business-question framing.

Next step: Build one portfolio project around a real operating problem such as churn, lending, pricing, or workforce planning, and make sure the repo includes SQL, Python, a dashboard, and a short business memo.

Mid-Career Candidates

Difficulty: Moderate to high, but better than for new grads because about 50% of the recent local sample was mid-level and about 35% was senior.[13]

Best target: Aim at business-facing analytics, decision support, and ML-enabled roles inside professional services or finance-adjacent employers, where sector conditions are stronger.[9][27][3]

Biggest mistake: Presenting yourself as a tools person instead of a problem owner who can turn data into revenue, risk, customer, or operations decisions.

Next step: Rewrite your resume around 4-6 quantified wins, then tailor a version for BI-heavy roles and a separate version for ML or decision-science roles.

Career Switchers

Difficulty: High, because most postings that stated an education requirement asked for a bachelor's degree, about 30% asked for a master's degree, and the role mix still leaned mid and senior.[28][13]

Best target: Switch first into reporting, BI, revenue operations, or finance-adjacent analytics rather than jumping straight to ML engineer or AI engineer titles.

Biggest mistake: Leading with coursework alone instead of proof that you can work with messy business data and communicate decisions to nontechnical stakeholders.

Next step: Bridge through your current domain by building analytics artifacts in that domain, then target roles where subject knowledge can offset a shorter technical track record.

Salary Reality

high pay highly concentrated

Observed local wage data is strongest for data scientists, not the whole category: BLS puts Salt Lake City-Murray at a $114,990 median, $117,140 mean, $81,260 at the 25th percentile, and $146,160 at the 75th percentile in 2025.[15][16][17][18] Directional offered-pay evidence is lower: Revelio Public Labor Statistics shows a mean offered salary on new Utah openings of about $103,877 in September 2026 based on n=261, versus about $124,229 nationally based on n=44,364.[19]

This is a good-paying market, but not a pay leader on every measure. Local data scientist mean pay trails the national mean of $126,800, and the metro-wide computer and mathematical mean hourly wage of $53.93 was below the national $57.73 in May 2025.[20][21]

The tradeoff is access. The local sample showed more than 30 postings across more than 20 companies over the last 90 days, about 65% on-site, about 35% hybrid, about 0% remote, and only about 20% entry-level.[22][14][13]

Best-paying path: The strongest pay tends to sit in specialized data science and AI work rather than broad analyst work: local data scientist pay reaches $159,820 at the 90th percentile, while nationally computer and information research scientists sit at a $140,300 median and $153,930 mean.[23][24][25]

Caution: Do not overread the top end. Those figures describe a narrower, more senior subset of roles, while the newer salary signals are mean offers rather than medians and are statewide rather than Salt Lake City-only.[19]

Where the Opportunities Are Concentrated

Real opportunity is concentrated less in headline "AI engineer" branding and more in employers that need analytics inside operating businesses. Salt Lake City-Murray had 154.9 thousand jobs in professional and business services in August 2026, up 4.3098% year-over-year, and Utah financial activities employment was 102.4 thousand, up 1.5% year-over-year.[9][27] Those are the environments where BI, forecasting, risk, customer analytics, and decision-support work usually lands. Pure information-sector tech is the weaker pocket right now. Local information employment was 22.4 thousand in August 2026, down -6.6667% year-over-year.[10] That does not eliminate product analytics or ML roles, but it does mean fewer easy-entry seats and more competition when a strong brand-name opening appears. The recent local sample reinforces that selectivity. It showed more than 30 postings across more than 20 companies, led by UTN, SubcontractorHub, and Snap Finance, with about 50% of roles at mid level and about 35% at senior level.[22][3][13]

• Professional services and operations analytics (high): Metro professional and business services employment was up 4.3098% year-over-year, and the local skill mix emphasized SQL, Python, Tableau, and Excel.[9][26]
• Finance, lending, and risk-adjacent analytics (moderate): Utah financial activities employment was up 1.5% year-over-year, and Snap Finance appeared among the most consistently active named employers in the recent local sample.[27][3]
• Pure tech-product and information-sector AI roles (limited): Local information employment was down -6.6667% year-over-year, and the recent local sample showed about 0% remote work.[10][14]

Where to focus: Focus first on business-embedded analytics roles that use SQL, Python, and visualization to improve revenue, operations, lending, or customer decisions.

Skills and Credentials Worth Pursuing

• SQL (table stakes): SQL appeared in about 65% of the recent local posting sample, making it the clearest baseline skill in this market.[26]
• Python (table stakes): Python showed up in about 60% of recent local postings and also appears in broader U.S. hiring guidance as an in-demand analytics skill.[26][30]
• Tableau / Power BI (differentiator): Tableau appeared in about 25% of the recent local sample, and national hiring guidance separately highlights Power BI as an in-demand analytics skill.[26][30]
• Machine learning (premium): Machine learning appeared in about 20% of the recent local sample, and broader hiring guidance still flags machine learning and data science as in-demand specialties.[26][30]
• Databricks (premium): Databricks showed up in about 15% of the recent local sample, which suggests employers reward candidates who can work beyond desktop BI tools.[26]
• Data visualization and Excel (table stakes): Excel and data visualization each remained visible in the recent local skill mix, signaling that many employers still hire for decision support, reporting, and stakeholder communication rather than pure modeling alone.[26]
• Governance, privacy, and responsible AI (differentiator): National hiring guidance calls out governance and privacy expertise, and the University of Utah's recent responsible-AI events point to visible local interest in this layer of work.[30][31]
• TOGAF (differentiator): TOGAF was the most commonly cited certification in the recent local sample, but it appeared in only about 5% of postings, so it is niche rather than universal.[32]

Adjacent Roles to Consider

• FP&A analyst (pivot): It uses forecasting, reporting, and business-partnering skills that overlap strongly with analytics work.
• Revenue operations analyst (bridge): It lets you use SQL, dashboarding, funnel analysis, and process metrics without needing to win a pure data-science title first.
• Market research analyst (pivot): It is a practical neighbor for candidates whose strengths are survey analysis, customer insight, segmentation, and storytelling rather than heavy modeling.
• Risk or fraud analyst (both): This path fits candidates who want analytical work in lending, compliance, or operations-heavy finance settings.
• Product operations analyst (bridge): It keeps you close to tech organizations while emphasizing instrumentation, reporting, experimentation, and cross-functional execution.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two tracks: one for BI and business analytics roles, one for data science or ML roles.
• Publish one portfolio piece that starts with a business question, shows the SQL and Python workflow, and ends with a dashboard plus a decision memo.
• Create a target list of 25 Salt Lake-area employers across finance, business services, healthcare, and university-linked organizations rather than applying only to big-tech labels.
• Audit every application asset for local reality: if your materials imply remote-only interest, fix that before applying.

Days 31-60

• Add one domain-specific case study in lending, subscriptions, healthcare operations, or workforce analytics so your work matches the local demand mix more closely.
• Build a reusable interview pack with one dashboard walk-through, one forecasting or experimentation example, and one messy-data cleanup story.
• Reach out to hiring managers and analytics leaders with a short note tied to a real metric problem their business likely faces, instead of generic networking asks.
• If you are missing a visualization tool, pick Tableau or Power BI and get to production-level comfort, not just tutorial familiarity.

Days 61-90

• If pure data science traction is weak, deliberately pivot applications toward revenue operations, FP&A-adjacent analytics, risk analytics, or product operations roles.
• Add a second portfolio artifact that shows collaboration and business communication, such as a requirements brief, KPI tree, or experiment readout.
• For mid-career candidates, prepare a quantified case for on-site or hybrid value, since local remote supply is scarce.
• If you are targeting AI-heavy roles, add a governance or responsible-AI project so you are not competing only on generic model-building claims.

Methodology and Confidence

This September 2026 report was generated on October 10, 2026. Latest direct national data: September 2026. Latest direct Salt Lake City-Murray, UT data: September 2026.

Confidence: Overall confidence: High. Recent local occupation, wage, and labor-market context data are available.

Limitations

• Local wage and concentration data is strongest for data scientists, so it is a useful anchor but not a perfect live read on every BI, analytics engineering, ML, or decision-science niche in Salt Lake City-Murray.
• Some of the freshest local labor-market context comes from late-summer government releases that can still be revised, so month-to-month readings should be treated as directional rather than final.
• Statewide occupation trend data was used as a proxy where metro-level occupation trend data is not published, which means the Utah direction signal may not map perfectly to Salt Lake City-Murray alone.
• The representative titles in this report stand in for a broad family of work, so no single title—especially data scientist—should be treated as the whole market.
• The Callings.ai job database is a partial, deduplicated sample of online postings, so direction of demand, leading employer names, and skill patterns are more reliable here than exact counts or exact shares.

References

1. Reveliolabs. Mass-layoff Notices — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
2. Sltrib. A Utah tech darling’s A.I. business just changed hands. Now, 175 local workers are losing their jobs. · 2026-10 · sltrib.com
3. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
4. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
5. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
6. Federal Reserve Economic Data. Consumer Price Index for All Urban Consumers: All Items in U.S. City Average · 2026-08 · fred.stlouisfed.org
7. Federal Reserve Economic Data. Average Hourly Earnings of All Employees, Total Private · 2026-09 · fred.stlouisfed.org
8. Federal Reserve Economic Data. Federal Funds Effective Rate · 2026-09 · fred.stlouisfed.org
9. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
10. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
11. Reveliolabs. Job Openings — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
12. Reveliolabs. Employment — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
13. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
14. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
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 · data.bls.gov
19. Reveliolabs. Salaries — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
20. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
21. Bureau of Labor Statistics. Occupational Employment and Wages in Salt Lake City-Murray — May 2025 : U.S. Bureau of Labor Statistics · 2026-08 · bls.gov
22. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
23. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
24. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
25. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
26. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
27. Federal Reserve Economic Data. All Employees: Financial Activities in Utah · 2026-08 · fred.stlouisfed.org
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. Salaryguide-americas. U.S. - 2026 Salary & Hiring Trends Guide | Hays · 2026-01 · salaryguide-americas.hays.com
31. Attheu. One-U Responsible Artificial Intelligence Initiative Symposium on Oct. 6 - @theU · 2026-09 · attheu.utah.edu
32. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
33. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
34. 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 Salt Lake City-Murray, UT from the live Callings.ai job index. Browse current Data, Analytics & AI openings in Salt Lake City-Murray, UT.