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

Executive Verdict

Market rating: competitive | Confidence: High

Denver is still a viable market for Data, Analytics & AI, but it is not an easy one right now. Local unemployment was 4.6% in August 2026, up from 3.8% a year earlier.[30][31] Metro Information employment was down 7.9399% year over year in August 2026, while Professional and Business Services was essentially flat at 0.1885% growth.[4][32] At the same time, statewide Data, Analytics & AI postings were up 26.0% year over year in September 2026 even as statewide employment in the field was down 1.9%, which points to selective hiring and replacement demand more than a broad hiring boom.[11][12] Local posting mix also skews experienced, with about 50% senior roles and about 15% entry-level.[14]

Best positioned: The best odds right now go to mid-career candidates who can show Python, SQL, and machine-learning delivery and are open to hybrid or on-site roles in defense, consulting, or large-enterprise settings.[26][6][28]

Main caution: The biggest risk is assuming the AI narrative means lots of easy remote openings; Denver businesses report AI adoption above the national rate, but local openings are still mostly experienced and mostly not remote.[33][6][14]

What Changed Recently

• Competition got tougher locally: Denver-Aurora-Centennial unemployment reached 4.6% in August 2026, up from 3.8% a year earlier.[30][31]: That usually means more applicants per opening, especially for recognizable employers and entry-level roles.
• Colorado's Data, Analytics & AI market showed more openings but slightly less employment: active postings were up 26.0% year over year in September 2026, while employment in the field was down 1.9%.[11][12]: This is a selective market with real demand, but much of it looks like backfill, churn, and targeted hiring rather than broad expansion.
• The local industry backdrop diverged: Denver Information employment fell 7.9399% year over year in August 2026, while Professional and Business Services rose 0.1885%.[4][32]: That shifts the better odds toward consulting, contractor, and enterprise transformation teams rather than pure-tech employers.
• National conditions stayed steady rather than hot: U.S. 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%.[7][9][10]: Employers still fund priority analytics work, but finance teams are selective on headcount and pay.
• Denver metro AI adoption was reported at 26.8% in July 2026, above the 21.7% national rate, but adoption had only edged up 0.2 percentage points since January.[33]: AI fluency helps, but local adoption is not accelerating fast enough to count on an across-the-board hiring surge.

What This Means for You

Entry-Level Candidates

Difficulty: High for pure entry-level data-science roles; moderate for analytics roles tied to an existing domain.

Best target: Target analyst and BI-style roles inside consulting, defense, telecom, and enterprise teams where Python, SQL, and data visualization matter more than frontier-model research.[28][26]

Biggest mistake: Assuming a certificate alone will carry you; among postings that state education, about 75% ask for a bachelor's degree, and the local mix is only about 15% entry-level.[29][14]

Next step: Build one portfolio case that starts with messy business data in SQL, analyzes it in Python, and ends with a dashboard plus a written recommendation.

Mid-Career Candidates

Difficulty: Competitive, but winnable if you have clear business impact and can work hybrid or on-site.

Best target: Senior analyst, decision scientist, data scientist, and applied ML roles that sit close to operations, contracts, or transformation work.

Biggest mistake: Leading with tools instead of outcomes; Denver employers need proof that your analysis changed revenue, cost, risk, or speed.

Next step: Rewrite your resume around 3-5 quantified wins and tailor separate versions for contractor/consulting roles and enterprise roles.

Career Switchers

Difficulty: High unless you bring a strong domain story from finance, healthcare, telecom, public sector, or operations.

Best target: Business-facing analytics roles where your prior industry knowledge can beat a cleaner technical profile.

Biggest mistake: Applying only to data scientist titles instead of bridge roles that reward domain fluency.

Next step: Pick one adjacent landing role, build a portfolio around problems from your prior field, and use that to enter the market sideways.

Salary Reality

high pay highly concentrated

Government wage data puts Denver data scientists at a $112,520 median annual wage and $125,040 mean, with a 25th-75th percentile band of $97,180 to $147,950 in 2025.[17][18][19][20] The small local computer-and-information-research-scientist segment—a rough proxy for more advanced AI research work—paid a $133,970 median and $144,500 mean, but the metro only had 160 such jobs.[21][22][23] Directional pay signals align with that: Colorado's mean offered salary on new Data, Analytics & AI openings was about $118,076 in September 2026, and local posted ranges centered on about $106k to $172k.[24][25]

This is still good pay for Denver, but the money is not evenly distributed. Solid compensation shows up once you clear the Python/SQL baseline and can tie analysis or ML work to real business outcomes.

The tradeoff is access: about 50% of local postings skew senior, only about 10% are remote, and many better-paying roles sit in hybrid or on-site environments.[14][6]

Best-paying path: The strongest pay tends to sit in advanced AI/research work and senior ML-heavy roles, especially when paired with PyTorch, AWS, or clearance-adjacent requirements.[21][22][26][27]

Caution: Do not overread the top end: the highest BLS AI-style wage figures come from a very small local segment, and posted salary bands often cover multiple levels or premium employers.[23][25]

Where the Opportunities Are Concentrated

Real opportunity is not evenly spread across all data titles. Over the last 90 days, the local sample showed more than 125 postings across more than 75 companies, and the most-active industries were software development (about 25%), defense and space manufacturing (about 20%), business consulting (about 10%), telecommunications (about 10%), and IT services and IT consulting (about 10%).[42][28] The most consistently active employers were CACI, Booz Allen, Deloitte, Dataroles, and Loenbro, and hiring was fragmented rather than dominated by one employer.[13][3] The practical takeaway is that Denver demand appears strongest where analytics sits close to contracts, operations, and enterprise transformation—not only in consumer-tech product teams. That matches the local work-style mix as well: about 55% of postings were on-site, about 40% hybrid, and about 10% remote, while about 50% of roles skewed senior.[6][14] Candidates who can work across Python, SQL, machine learning, and business-facing communication should do better than candidates selling a single tool or a purely academic AI profile.[26][38]

• Defense and federal-contractor analytics (high): This lane shows up through employers like CACI and Booz Allen, and defense and space manufacturing accounts for about 20% of the local sample.[13][28][27]
• Consulting and transformation teams (high): Deloitte is among the more active employers, and business consulting represents about 10% of the local sample.[13][28]
• Telecom and large-enterprise operations analytics (moderate): Telecommunications and IT services/consulting each make up about 10% of the local sample, and the work is often hybrid or on-site rather than remote-first.[28][6]

Where to focus: Focus first on mid-to-senior analytics and applied-AI roles inside defense, consulting, and large-enterprise teams that want Python, SQL, and machine-learning delivery and will consider hybrid or on-site candidates.[13][28][6][26]

Skills and Credentials Worth Pursuing

• Python (table stakes): Python shows up in about 75% of local postings and now beats R as the clearer common language across analyst and AI work.[26][34]
• SQL (table stakes): SQL appears in about 55% of local postings, making it the most reliable way to prove you can work with real business data before any modeling starts.[26]
• Machine learning (differentiator): Machine learning appears in about 35% of local postings, and national BLS projections still show data scientists as a long-run growth field.[26][35]
• Generative AI and prompt design (differentiator): Generative AI appears in about 15% of local postings, and national tech hiring increasingly asks for AI fluency and better prompting rather than passive awareness.[26][36][37]
• Data visualization and decision storytelling (differentiator): Data visualization shows up in about 15% of local postings, and the analyst role is shifting toward framing questions and explaining decisions, not just building queries.[26][38]
• Data governance (premium): Governed, high-quality data is increasingly treated as a prerequisite for trustworthy analytics and AI, especially in regulated or enterprise environments.[39][40]
• TS/SCI clearance (premium): A small but real share of local postings ask for TS/SCI clearance, and Denver's employer mix includes a notable defense and space component.[27][28]
• MLOps and full lifecycle ML (premium): For machine-learning candidates, value is moving beyond model building toward deployment, monitoring, and lifecycle tooling such as MLflow and DVC.[41]

Adjacent Roles to Consider

• Business Analyst / Business Systems Analyst (bridge): This is a practical bridge if you can translate requirements, metrics, and process problems into structured analysis.
• Revenue Operations Analyst (both): Good fit for candidates with SQL, dashboards, funnel analysis, and business communication skills.
• Risk or Fraud Analyst (pivot): This is a sensible pivot for people who like anomaly detection, rules, forecasting, and decision support.
• Market Research Analyst (pivot): It uses many of the same analytic muscles but sits in a different job family focused on customer and market insight.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two versions: one for analytics/BI work and one for applied ML/AI work, each led by quantified business outcomes rather than tool lists.
• Build a target list of 30 Denver-area employers across defense contractors, consulting firms, telecoms, and large enterprises instead of mass-applying to generic remote listings.
• Create one portfolio project that shows the full workflow: SQL extraction, Python analysis, a dashboard, and a short decision memo for a nontechnical stakeholder.
• Set a 7-day application SLA for saved jobs; the typical active posting has been open around 25 days, so late applications are easier to crowd out.[15]

Days 31-60

• Add one domain-specific case study tied to a real Denver-heavy segment such as defense operations, telecom performance, or enterprise cost/risk analysis.
• Practice a 5-minute narrative for each project that explains the business question, the tradeoffs, and what changed because of your analysis.
• If you are missing a clear differentiator, choose one now: clearance-adjacent work, governance/compliance, or applied GenAI workflow design.
• Attend Denver Tech Week's "From Prototype to Production: Building Enterprise AI Agents" session or similar local meetups to meet applied-AI practitioners and hiring managers.[16]

Days 61-90

• Broaden your search to adjacent landing roles if interviews are thin, especially business analyst, revenue operations analyst, or risk/fraud analyst roles.
• Ask every recruiter screen about work arrangement early; the market is mostly on-site or hybrid, so commute friction can quietly kill matches.
• For mid-career roles, bring a one-page impact sheet to interviews with 3-5 quantified wins in revenue, cost, risk, or speed.
• If you keep missing final rounds, collect patterns by segment and retune your positioning toward one market lane instead of trying to sound universal.

Methodology and Confidence

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

Confidence: Overall confidence: High. The local picture is supported by recent government labor data, current metro context data, and fresh employer-composition signals.

Limitations

• Local occupation wage and employment estimates are the best anchor here, but detailed Denver occupation pay data lags current hiring conditions; most role-specific wage figures in this report are from 2025, while the broad labor-market context is current through August 2026.
• This category covers several sub-roles with different pay and hiring patterns, so data-scientist and computer-and-information-research-scientist figures should be read as anchors for the broader Data, Analytics & AI market rather than a precise wage for every title.
• Some direction-of-demand signals are only published at the Colorado level, so statewide movement may not match Denver exactly in a given month.
• Recent unemployment, labor-force, and year-over-year change figures can still be revised, so sharp short-term moves should be treated as provisional rather than final.
• The Callings.ai job database is a partial, deduplicated sample of online postings, so direction of demand, leading employer names, and recurring skill patterns are more reliable than exact counts, exact shares, or a full census of Denver openings.

References

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Live Openings in This Market

This report is published monthly; its companion page tracks the active Data, Analytics & AI openings in Denver-Aurora-Centennial, CO from the live Callings.ai job index. Browse current Data, Analytics & AI openings in Denver-Aurora-Centennial, CO.