Is Data, Analytics & AI a Good Job Market in Denver-Aurora-Centennial, CO?

Executive Verdict

Market rating: competitive | Confidence: High

Denver is still a workable market for Data, Analytics & AI, but it is not an easy one. Colorado-wide occupation data shows active postings up 14.3% year-over-year even as employment in the field is down 0.8%, while Denver's Information sector fell 5.9% and Professional and Business Services slipped 0.4% in March 2026.[6][7][4][8] That mix points to selective hiring for specific skills rather than broad-based team expansion. We also observed more than 200 postings across more than 125 companies in the last 90 days, so the market is active enough to search, just not forgiving.[9]

Best positioned: Mid-career candidates who can show shipped work in Python, SQL, and machine learning, and who are open to on-site or hybrid roles at large employers, have the best odds right now.[10][11][12][13]

Main caution: The biggest mistake is treating Denver like a remote-first entry market; only about 15% of sampled roles were remote and only about 15% were entry level.[11][13]

What Changed Recently

• Colorado's Data, Analytics & AI openings are up 14.3% year-over-year, but employment in the field is down 0.8%.[6][7]: That usually means more backfills, tighter screening, and targeted AI or analytics bets rather than broad team growth.
• Denver's Information employment fell 5.9% year-over-year in March 2026, and Professional and Business Services was down 0.4%.[4][8]: Those are two major homes for analytics talent, so interview cycles are likely to stay selective even when roles are open.
• The local sample still shows more than 200 postings across more than 125 companies over the last 90 days, and hiring is fragmented rather than concentrated in one dominant employer.[9][14]: A broad employer list and steady application cadence will work better than waiting on a few famous names.
• Denver had public layoff notices tied to PNC Bank affecting 777 employees beginning June 2026 and to an undisclosed financial-services employer affecting 95 employees beginning May 2026, while Colorado recorded 21 WARN-eligible notices and about 2,410 workers notified in April 2026.[1][2][3]: Finance-linked analytics teams may be less predictable than the headline posting volume suggests.
• National unemployment was 4.3% in April 2026, payrolls were up 0.2% year-over-year, CPI was up 3.1% in March, the fed funds rate was 3.64%, and average hourly earnings were up 3.6% year-over-year.[15][16][17][18][19]: For Denver job seekers, that reads like a slow-growth economy where employers are still hiring but remain cost-conscious on headcount and pay.

What This Means for You

Entry-Level Candidates

Difficulty: Hard.

Best target: On-site or hybrid analyst, BI, reporting, and decision-support roles inside larger employers, healthcare-tech teams, or consulting-style organizations.

Biggest mistake: Applying straight to AI engineer or data scientist titles without a portfolio that proves production-ready SQL, Python, and business storytelling.

Next step: Build one portfolio project that combines SQL, Python, and a dashboard, then tailor applications toward analyst and BI titles before moving upmarket.

Mid-Career Candidates

Difficulty: Competitive but very workable.

Best target: Mid-to-senior analytics, decision science, analytics engineering, and applied data science roles where you can show ownership of experiments, models, or stakeholder-facing decisions.

Biggest mistake: Using one generic resume for analyst, science, and ML roles.

Next step: Create separate resume versions for analytics, data science, and ML-flavored roles, and lead every bullet with measurable business outcomes.

Career Switchers

Difficulty: Hard unless you bring domain depth.

Best target: Domain-led analyst roles in industries you already understand, especially healthcare, finance, telecom, or enterprise operations.

Biggest mistake: Trying to compete head-on with experienced candidates for pure data scientist titles.

Next step: Translate your prior domain work into metrics ownership, reporting logic, forecast decisions, or experimentation results, then pursue analyst-adjacent roles first.

Salary Reality

high pay highly concentrated

Observed local posted salary ranges center on about $105k to $158k, with a broader 25th-75th band of about $86k to $200k.[20] As directional cross-checks, Colorado's mean offered salary on new Data, Analytics & AI openings was about $119,047 in April 2026, Denver data scientist pay is estimated at $125,280/year, and a technology-focused Denver data analyst low-end figure is $115,500/year.[21][22][23]

This is a well-paid category in Denver. The catch is that pay is being earned through specialization and experience, not through easy access.

The upside is offset by selective hiring, a market that skews toward mid and senior roles, and a strong on-site bias. High pay is there, but the market does not look broad or beginner-friendly.

Best-paying path: The best-paying path tends to be senior data science, analytics engineering, and ML-heavy work. Local postings request machine learning in about 35% of roles, with PyTorch and TensorFlow each appearing in about 15%.[10]

Caution: Do not overread top-end numbers. The local posted band reaches about $200k and national top-end data scientist pay exceeds $194,410, but those figures mainly reflect senior, niche, or leadership-caliber roles.[20][24]

Where the Opportunities Are Concentrated

Real opportunity is concentrated in tech-led and enterprise analytics environments rather than in a single dominant employer. In the local sample, technology accounts for about 45% of postings and information technology about 25%, with healthcare at about 10% and healthcare technology at about 5%.[28] Hiring is fragmented across employers, and about 40% of postings come from large employers with another about 25% from enterprise firms.[14][12] The most consistently active names include CACI, Migrate Mate, R Systems International Limited, R Systems, Dish Network Corporation, Ibotta, Inc., and RVO Health.[29] That mix rewards candidates who can show both technical depth and business usefulness. Mid-level roles make up about 45% of the sample and senior roles about 35%, so many openings are really for people who can own pipelines, experiments, stakeholder communication, or production ML work without much ramp time.[13] This is why the market feels open on paper but tougher in practice, especially for generalists and first-job seekers.

• Large tech and enterprise analytics teams (high): Best match for candidates with strong Python, SQL, stakeholder communication, and a track record of shipping work.
• Healthcare and health-tech analytics (moderate): A practical target for analysts who can mix reporting, operations understanding, and data storytelling.
• Defense, regulated, and enterprise data environments (moderate): Good fit for candidates who are comfortable with structure, compliance, or complex stakeholder environments.
• Remote-only search (limited): Viable, but much narrower than many applicants expect.

Where to focus: Prioritize mid-level Python/SQL roles at large or enterprise employers in tech, healthcare-tech, and regulated industries, and widen your search to on-site and hybrid openings.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appears in about 60% of local postings, making it the most reliable screening skill across analyst, science, and AI-flavored roles.[10]
• SQL (table stakes): SQL shows up in about 50% of local postings, so it remains core even when roles are branded as AI or data science.[10]
• Machine learning (premium): Machine learning is requested in about 35% of local postings, which is high enough to separate stronger candidates from general reporting profiles.[10]
• Data visualization (differentiator): Data visualization appears in about 15% of local postings, and it is often the skill that makes technical work legible to hiring managers and stakeholders.[10]
• PyTorch or TensorFlow (premium): PyTorch and TensorFlow each appear in about 15% of local postings, which signals real demand for applied model-building rather than analytics-only work.[10]
• Experimentation and data modeling (differentiator): Current salary guidance highlights experimentation and data modeling alongside Python, cloud, and machine learning as important for stronger compensation in 2026.[25]
• Data science or big data certification (differentiator): Formal certifications are rarely mandatory locally—certified data scientist appears in less than 5% of postings—but data science and big data certifications are associated with an average 17.9% pay boost in 2026 salary guidance.[26][27]

Adjacent Roles to Consider

• Product analyst (bridge): It uses SQL, experimentation, and business storytelling without requiring the same level of model-building depth as many data science roles.
• Revenue operations analyst (bridge): It rewards strong reporting, dashboarding, and process analytics skills while giving you a clearer path in sales-led organizations.
• Marketing analyst or market research analyst (pivot): It is a clean pivot for candidates who are stronger in segmentation, campaign analysis, or customer insight than in ML production work.
• FP&A or risk analyst (pivot): This path lets you reuse SQL, reporting, and forecasting skills in finance-heavy environments.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into three versions: analyst/BI, data science, and ML-heavy. Do not use one catch-all document.
• Build one Denver-relevant portfolio case study using SQL, Python, and a dashboard, ideally tied to healthcare, telecom, enterprise ops, or regulated data.
• Create a target list of 40 employers, anchored by the locally active names and similar large or enterprise firms.
• Add a short project note to your LinkedIn headline or portfolio that explicitly uses the keywords Python, SQL, machine learning, and data visualization.

Days 31-60

• Expand beyond direct applications into recruiter-led, consulting, and contract channels for analytics work.
• Practice interview stories around stakeholder conflict, messy data, experimentation, and model tradeoffs, not just tooling.
• Publish one short write-up that shows how you move from raw data to a business decision, because this market rewards usefulness over pure theory.
• If you need a credential, choose one tied to data science or big data only after your portfolio is strong enough to support it.

Days 61-90

• Widen your role mix to include product analyst, rev-ops analyst, finance analyst, or marketing analyst paths if direct data-science traction stays weak.
• Reassess your location strategy: if you have been remote-only, add on-site and hybrid applications immediately.
• Target industries separately with tailored examples, such as healthcare operations, enterprise reporting, or ML use cases, instead of sending the same projects everywhere.
• If you are still stalled, take a bridge role that gives you production data ownership, then reposition after 6 to 12 months.

Methodology and Confidence

This April 2026 report was generated on May 10, 2026. Latest direct national data: April 2026. Latest direct Denver-Aurora-Centennial, CO data: May 2026.

Confidence: Overall confidence: High. The report is supported by recent local labor data, local hiring composition signals, and national macro context.

Limitations

• The strongest metro-level data here is broad labor-market and sector data through March 2026, not a metro-only count of Data, Analytics & AI workers, so some occupation-specific direction had to be inferred from Colorado-wide occupation data.
• Several early-2026 government year-over-year figures are preliminary and may be revised, so small declines or gains should be treated as directional rather than final.
• This category covers analyst, BI, decision science, data science, analytics engineering, and AI/ML work, and those sub-roles can move differently even when the overall market looks stable.
• Some pay figures in this report come from posted salary ranges or salary guides rather than settled wages, so they are best read as negotiation ranges and market signals, not guaranteed outcomes.
• The Callings.ai job database is a partial, deduplicated sample of online postings, so it is more reliable for direction of demand, leading employer names, work arrangement, and skill patterns than for exact hiring totals or precise market share.

References

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