Is Data, Analytics & AI a Good Job Market in New York-Newark-Jersey City, NY-NJ?

Executive Verdict

Market rating: competitive | Confidence: Medium

This is still a worthwhile market if you already match what employers are buying. New York statewide Data, Analytics & AI postings were up 27.5% year over year in July 2026, category employment was up 0.6%, and the metro showed more than 1,200 postings across more than 700 companies over the last 90 days.[5][14][16] The catch is selectivity: about 45% of sampled postings were mid-level, about 30% senior, about 15% lead+, and only about 10% entry-level, while about 50% were on-site and about 40% hybrid.[1][29] That makes this a good market for proven practitioners, but a harder one for new grads and broad, undifferentiated applicants.

Best positioned: The best odds right now are for mid-career analysts, analytics engineers, and data scientists who can show Python, SQL, dashboards or applied ML, plus business-facing delivery in finance, consulting, healthcare, or operations.[6][7][3]

Main caution: Do not confuse strong salary headlines with easy access: posted pay is high, but the market is skewed toward experienced candidates and mostly non-remote roles.[8][1][29]

What Changed Recently

• Statewide hiring demand in Data, Analytics & AI strengthened faster than the broader market: active postings in New York were up 27.5% year over year in July 2026, while postings across all occupations in the state were down 1.7%.[5]: That means this category is outperforming the general New York job market, so it is still worth prioritizing if your skills are already market-ready.[5]
• The metro's Professional and Business Services base grew 1.3285% year over year in June 2026, while Information employment was nearly flat at 0.1980%.[13][12]: That tilts the better local lanes toward consulting, client-services, and business-facing analytics rather than relying only on pure information-sector employers.[13][12][7]
• Recent local postings keep emphasizing practical analytics execution: LTIMindtree's Jersey City role centered on collecting, cleaning, and validating data, and a featured New York senior analyst role emphasized SQL, BI tools, dashboards, and reporting automation.[2][3]: Employers appear to be paying for hands-on delivery and decision support, not just general AI interest.[2][3]
• The local sample remains experience-heavy: about 45% of postings were mid-level, about 30% senior, about 15% lead+, and only about 10% entry-level over the last 90 days.[1]: If you are early career, you need a narrower wedge such as reporting, data quality, or business-analyst-adjacent work instead of applying broadly across the whole category.[1][2][4]
• Nationally, payroll growth was slow at 0.1993% year over year in July 2026 and unemployment was 4.3% in April 2026.[23][22]: For New York applicants, that usually means budgets still exist, but interview loops stay selective and teams prefer candidates who can be productive quickly.[23][22]

What This Means for You

Entry-Level Candidates

Difficulty: Hard. Only about 10% of sampled postings were entry-level, and most demand clusters around mid and senior work.[1]

Best target: Aim for BI/reporting, data quality, and business-analyst-adjacent roles that lean on SQL, dashboards, validation, and decision support rather than pure model-building.[2][3][4]

Biggest mistake: Applying as a generic aspiring data scientist without a narrow proof-of-work portfolio in reporting automation, dashboarding, or data QA.

Next step: Build two market-matching case studies in the next month: one SQL/dashboard project and one data-cleaning or validation project tied to a business KPI, then rewrite your resume around those artifacts.[2][3]

Mid-Career Candidates

Difficulty: Moderate but competitive. The market has meaningful demand, yet it is crowded and skewed toward candidates who already match the stack.[5][1][6]

Best target: Target analytics engineer, senior data analyst, decision science, and applied data science roles in consulting, financial services, healthcare, and tech.[7][3]

Biggest mistake: Leading with tools only instead of showing business outcomes, stakeholder influence, and production-ready reporting or modeling.

Next step: Reposition your resume around three outcome stories tied to revenue, risk, cost, or operations improvement, with Python, SQL, and domain context explicit in every bullet.[6]

Career Switchers

Difficulty: Harder than it looks. This market pays well, but it usually buys prior domain context plus analytics execution, not beginner-level curiosity.[8][1]

Best target: Switch through your current industry into analyst, BA, reporting, or warehouse-adjacent roles instead of jumping straight to AI engineer titles.[2][9]

Biggest mistake: Trying to outrun experience requirements by stacking certificates; most postings that mention certifications still do not require one.[10]

Next step: Choose one domain lane such as finance, healthcare, operations, or consulting-style analytics, and build a portfolio that mirrors that employer's data problems and vocabulary.[7]

Salary Reality

high pay highly concentrated

Observed local posting data puts the center of advertised pay at about $135k to $185k for the broader category, with hourly roles around about $43 to $58 / hour.[8][31] More targeted signals are lower or narrower for some analyst work, such as a Union City data-analyst listing at $90k-125k, while older metro data-scientist wage benchmarks show a $135,980 median and $144,700 mean from the May 2025 BLS release.[4][32] Statewide new-opening pay from Revelio Public Labor Statistics was ~$133,513 in July 2026 based on n=5,071, which is useful as a fresh directional benchmark rather than a metro-specific median.[15]

This is a high-paying market for the category. New York's statewide mean offered salary for Data, Analytics & AI of ~$133,513 sits well above the statewide all-occupation offered-salary figure of ~$92,417, so employers are paying a real premium for analytics and AI capability.[15]

The pay upside is offset by access barriers: only about 10% of sampled postings were entry-level, only about 10% were remote, and the most-requested skills still start with Python and SQL.[1][29][6]

Best-paying path: The strongest compensation appears to sit in data scientist, AI/ML-adjacent, analytics engineering, and warehouse-platform-facing work. Robert Half lists national data scientist pay at $121,750 low, $153,750 mid, and $182,500 high, and a local recruiter signal showed a Data Warehouse Analyst role reaching up to $150,000.[26][9]

Caution: Do not overread the top end. Local salary bands combine multiple sub-roles with very different seniority and specialization, and some metro wage figures in the bundle are older than the current hiring cycle.[8][32][19]

Where the Opportunities Are Concentrated

Opportunity is spread across a long employer tail rather than locked inside one or two giants. The local sample shows more than 1,200 postings across more than 700 companies over the last 90 days, and hiring is described as fragmented across employers.[16][18] Named leaders in the sample include RevOps Advisor, Capital One Group, Deloitte, and Ernst & Young LLP, which tells you this market rewards business-facing analytics as much as pure research work.[17] Industry mix matters. Technology accounts for about 25% of sampled postings, financial services about 20%, software development about 15%, healthcare about 15%, and finance about 10%.[7] Combined with stronger metro growth in Professional and Business Services than in Information, that suggests many of the best openings sit in client delivery, risk, reporting, revenue, and operational decision-making rather than lab-style AI work.[13][12][7] Treat New York as a multi-lane market, not a single AI market. If you only chase remote AI titles, you are fishing in the smallest pond; about 50% of sampled roles were on-site, about 40% hybrid, and about 10% remote.[29]

• Consulting and client-delivery analytics (high): Deloitte and Ernst & Young LLP are among the most consistently active named employers, and metro Professional and Business Services employment grew 1.3285% year over year.[17][13]
• Financial-services and risk analytics (high): Capital One Group is one of the most active employers in the sample, and financial services plus finance account for about 30% of sampled industry demand when combined.[17][7]
• Healthcare and operational reporting (moderate): Healthcare is about 15% of the local posting mix, and local postings still stress data availability, quality assurance, dashboards, and decision support.[7][4][3]
• Pure remote AI-first roles (limited): Only about 10% of sampled roles were remote, and the market skews mid-level and above, which limits easy-access options for applicants chasing remote-only AI titles.[29][1]

Where to focus: Focus first on hybrid roles in consulting, financial services, and healthcare that ask for Python, SQL, dashboards, data quality, or applied ML tied to business decisions.[7][29][6][2][3]

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appears in about 70% of sampled local postings, making it the clearest baseline screen for this market.[6]
• SQL (table stakes): SQL shows up in about 50% of sampled local postings, and recent local roles emphasize data cleaning, validation, and reporting automation.[6][2][3]
• BI tools, dashboards, and reporting automation (differentiator): A featured senior analyst role in New York emphasized SQL, BI tools, dashboards, and reporting automation, signaling that employers want decision-ready outputs, not just analysis notebooks.[3]
• Machine learning fundamentals (premium): Machine learning appears in about 25% of local postings, and broader 2026 guidance says ML basics are no longer optional for most mid-level analytics roles.[6][24]
• AWS and cloud data-stack familiarity (differentiator): AWS appears in about 15% of sampled local postings, and adjacent national guidance shows analytics-engineering and data-engineering-adjacent work continues to command strong pay.[6][25][26]
• Data quality, validation, and governance (differentiator): Recent local postings highlighted collecting, cleaning, validating, quality-assuring, and making data available, which is a strong sign that trustworthy data work still converts to interviews.[2][4]
• Generative AI, prompt engineering, and AI workflow automation (premium): Forecasts for 2026 point to growing demand for generative AI, prompt engineering, and AI agents-workflow automation, and Gartner expects 40% of enterprise applications to include task-specific AI agents by the end of 2026.[27][28]
• Certifications (table stakes): They are not a major screening lever here because the most common certification requirement in sampled local postings was none, at less than 5%.[10]

Adjacent Roles to Consider

• Business Analyst (bridge): A Jersey City LTIMindtree posting explicitly blended Data Analyst and BA work, which shows employers are willing to buy hybrid analytics plus stakeholder-facing skills.[2]
• Revenue Operations Analyst (pivot): RevOps Advisor was the most consistently active named employer in the local sample at more than 40 postings, which suggests revenue and operations analytics is a real hiring lane in this market.[17]
• Data Warehouse Analyst (both): A local recruiter signal identified a Data Warehouse Analyst role reaching up to $150,000, showing that warehouse and platform-adjacent work can be a practical neighboring path.[9]

30 / 60 / 90-Day Plan

First 30 Days

• Pick one lane only: consulting analytics, financial-services analytics, healthcare analytics, or warehouse-adjacent analytics. Rewrite your headline, summary, and core bullets around that lane.
• Build two portfolio artifacts that mirror local demand: one SQL plus dashboard automation project and one data-cleaning or data-quality project with a business KPI before-and-after story.
• Create a target list of 30 employers split across large firms and long-tail buyers, with separate messaging for consulting, finance, and operating-company teams.
• Stop applying to remote-only searches as your main strategy and add hybrid and on-site commutable roles to your weekly pipeline.

Days 31-60

• Ship a case-study resume version for each target lane, with three quantified outcome bullets at the top and stack keywords embedded naturally.
• Run a weekly direct-outreach sprint to hiring managers and team leads at firms similar to Capital One Group, Deloitte, Ernst & Young LLP, LTIMindtree, and healthcare analytics teams.
• Practice live SQL, dashboard walkthrough, and business-case interviews instead of spending that time on extra certificates.
• Add one adjacent skill upgrade that changes your odds: AWS basics, dbt-style modeling concepts, or a lightweight ML interpretation project.

Days 61-90

• Review your application data by lane, not in aggregate, and double down only on the lane that is producing interviews.
• If conversion is weak, widen into Business Analyst, RevOps Analyst, and Data Warehouse Analyst searches rather than endlessly refreshing the same data-scientist keywords.
• Use contract and hourly opportunities as a bridge if needed, especially if you need recent experience or a faster re-entry path.
• Refresh your portfolio with one domain-specific project based on the interview feedback you are actually getting, not the projects you assumed employers wanted.

Methodology and Confidence

This July 2026 report was generated on August 10, 2026. Latest direct national data: August 2026. Latest direct New York-Newark-Jersey City, NY-NJ data: August 2026.

Confidence: Overall confidence: Medium. Direct local occupation data for this exact category is limited, so some conclusions rely on broader local context and directional hiring proxies.

Limitations

• There is no fresh metro-level occupation count for this exact category, so this page leans on June 2026 metro labor context and August 2026 hiring proxies rather than a direct July 2026 occupation estimate.[11][12][13][2][3][4]
• Statewide Data, Analytics & AI signals from Revelio Public Labor Statistics were used as a proxy where metro-level occupation data is not published, which helps with direction but can blur differences between Manhattan, Jersey City, Newark, and the wider state.[14][5][15]
• 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 market shares.[16][17][18][6]
• This category groups analysts, BI, data scientists, analytics engineers, and AI-adjacent roles, so pay can look high partly because the sample mixes mid-level analyst openings with premium machine-learning and engineering-leaning jobs.[8][1][19]
• The recent WARN notices in Newark and Piscataway are real local risk signals, but they were not identified as Data, Analytics & AI layoffs specifically, so they should be read as general market caution rather than direct evidence that this category is weakening.[20][21]

References

1. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
2. Jobright. Data Analyst/BA @ LTIMindtree | Jobright.ai · 2026-07 · jobright.ai
3. Builtinnyc. Best Data & Analytics Jobs in NYC, NY 2026 | Built In NYC · 2026-08 · builtinnyc.com
4. Jobtoday. 379 Best index Jobs in Union City, New Jersey (August 2026) | JOB TODAY · 2026-08 · jobtoday.com
5. Reveliolabs. Job Openings — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
6. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
7. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
8. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
9. Callings. Data & AI Jobs in NYC: June 2026 Outlook | Callings.ai · 2026-08 · callings.ai
10. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
11. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
12. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
13. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
14. Reveliolabs. Employment — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
15. Reveliolabs. Salaries — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
16. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
17. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
18. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
19. Birjob. Data Analyst vs Data Scientist vs Data Engineer: The 2026 Decision Guide · 2026-01 · birjob.com
20. Roi-nj. Mars Wrigley leaving Newark for Chicago, dropping over 300 jobs | ROI-NJ · 2026-07 · roi-nj.com
21. Data. Data - warn_notice_layoff · 2026-07 · data.usatoday.com
22. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-04 · data.bls.gov
23. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-07 · data.bls.gov
24. Skillcureacademy. Top 10 Data Science Tools Every Professional Must Know in 2026? · 2026-04 · skillcureacademy.com
25. Course. Data Science Salary 2026: Ranges by Role & Level · 2026-01 · course.careers
26. Robert Half. 2026 Tech and IT Salaries and Compensation Trends · 2026-01 · roberthalf.com
27. Tredence. Data Science Solutions | AI and Data Analytics Services Company - Tredence · 2026-06 · tredence.com
28. Masterofcode. 350+ Generative AI Statistics [January 2026] · 2026-07 · masterofcode.com
29. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
30. Reveliolabs. Mass-layoff Notices — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
31. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
32. Elevatedtech. Data Scientist Salary in New York-Newark-Jersey City (2026): $135,980 Median, $144,700 Mean · 2026-01 · elevatedtech.us

Live Openings in This Market

This report is published monthly; its companion page tracks the active Data, Analytics & AI openings in New York-Newark-Jersey City, NY-NJ from the live Callings.ai job index. Browse current Data, Analytics & AI openings in New York-Newark-Jersey City, NY-NJ.