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 a real market, but not an easy one. The New York metro unemployment rate was 4.6% in May 2026, and the metro still showed more than 1,700 Data, Analytics & AI postings across more than 900 companies over the last 90 days.[28][6] Statewide signals are better for this specialty than for the broader market: Data, Analytics & AI postings in New York were up 21.9% year over year in June 2026 while employment in the field was up 1.0%, even as all-occupation postings in the state were down 3.6%.[13][14] The catch is selectivity: only about 10% of local postings are entry level, about 40% are mid-level, about 35% are senior, and only about 15% are remote.[3][4]

Best positioned: Candidates with 3-7 years of experience, strong Python and SQL, and proof that they can turn analysis into business decisions have the best odds, especially if they are open to hybrid or on-site roles.[20][19][4]

Main caution: The biggest mistake is assuming NYC salary bands mean broad access; posted ranges center on about $130k to $185k, but the market is still senior-skewed and specialization matters.[10][3]

What Changed Recently

• New York's Data, Analytics & AI demand is outperforming the broader state market: occupation-specific active postings were up 21.9% year over year in June 2026, while all-occupation postings in New York were down 3.6%.[13]: That means you should not read broad hiring softness as a blanket negative for this category; specialty data work is holding up better than the average job market.
• Employment in New York's Data, Analytics & AI workforce was up 1.0% year over year in June 2026, while national employment in the category was essentially flat.[14]: Local employers are still adding or retaining talent, but the growth rate is modest enough that they can be choosy about experience and domain fit.
• Nationally, job openings rose 3.8851% year over year in May 2026, but hires fell 2.9655% and quits fell 6.7539%.[15][16][17]: Expect more posted roles than actual offers, slower interview cycles, and less voluntary movement creating backfill openings.
• The local posting mix remains senior-skewed and office-tethered: about 40% of openings are mid-level, about 35% are senior, about 50% are on-site, about 35% are hybrid, and about 15% are remote.[3][4]: This market is better for experienced candidates who can commute than for entry-level job seekers holding out for fully remote work.
• AI is taking more of the repetitive execution work in data jobs, shifting the value of these roles toward interpretation, business problem framing, and decision support.[18][19]: Candidates who present themselves as insight translators and operators will usually compete better than candidates who lead with tools alone.

What This Means for You

Entry-Level Candidates

Difficulty: Hard.

Best target: Aim first at business-facing analyst and BI roles in finance, healthcare, and enterprise operations rather than pure AI scientist openings.[5]

Biggest mistake: Applying as a generalist with coursework only and no proof you can answer a real business question.

Next step: Build two portfolio pieces: one SQL/dashboard case and one Python analysis that ends with a recommendation a manager could act on.

Mid-Career Candidates

Difficulty: Moderate, if your domain story is clear.

Best target: Target mid-level data science, decision science, BI, and analytics roles with a domain angle, because the local mix leans mid and senior rather than entry.[3]

Biggest mistake: Positioning yourself as a generic 'data professional' instead of showing shipped outcomes, stakeholder ownership, and domain depth.

Next step: Rewrite your resume around business impact, then split your search into one primary lane and one adjacent lane so employers can place you quickly.

Career Switchers

Difficulty: Hard.

Best target: Adjacent business analyst, revenue operations, or healthcare operations roles are the cleanest bridge, especially where employers value metrics fluency and stakeholder work more than advanced modeling depth.[2][5]

Biggest mistake: Taking another broad course without producing public work samples, referrals, or a domain-specific story.

Next step: Use local communities and structured programs only if they produce a portfolio and contacts; NYC School of Data and selective local bootcamps can help with that if you treat them as proof-building, not as a substitute for proof.[9][12][11]

Salary Reality

high pay highly concentrated

The cleanest direct local wage anchor is older BLS data: Data Scientists in the metro had a median annual wage of $118,620 in May 2023, with a 25th-percentile wage of $93,710 and a 75th-percentile wage of $140,800.[29] Newer posting-based signals are higher: local posted salary ranges for the category center on about $130k to $185k, and Revelio Public Labor Statistics puts the mean offered salary on new openings for New York Data, Analytics & AI roles at about $134,352 in June 2026 (n=4,879).[10][30] A local recruiter guide also shows a Data Warehouse Analyst role reaching up to $150,000, but that is a role-specific placement signal rather than a market-wide average.[24]

This is still a high-pay market relative to New York openings overall, which averaged about $89,647 on new openings statewide in June 2026.[30] The money is real, but much of the upside sits in specialized AI/ML, senior analytics, and domain-heavy roles.

The tradeoff is access. Only about 10% of local postings are entry level, about 50% are on-site, and only about 15% are remote, so higher pay often comes with a longer search, tougher screening, and less flexibility.[3][4]

Best-paying path: The strongest pay tends to sit in senior or specialized roles tied to technology and financial services, which together account for about half of the local posting mix.[5]

Caution: Do not read the top of a posted range as likely take-home pay. This category mixes analyst, BI, data science, and AI work, and the broader posted 25th-75th band runs from about $100k to $240k, which signals wide variance by seniority and specialty rather than a single going rate.[10]

Where the Opportunities Are Concentrated

Real opportunity is spread across a long employer tail rather than a few dominant brands. Over the last 90 days, the metro showed more than 1,700 postings across more than 900 companies, hiring was fragmented across employers, and even the most consistently active named employer in the sample, RevOps Advisor, accounted for only more than 75 postings.[6][1][2] That is useful if you are willing to target many firms instead of waiting on a short list of famous employers. The work clusters by industry and by seniority. Technology accounts for about 30% of the local sample, financial services about 20%, information technology about 15%, healthcare about 10%, and software development about 10%; meanwhile about 40% of roles are mid-level and about 35% are senior.[5][3] In practice, that points toward business-facing analytics, decision support, revenue or risk analytics, and applied AI/ML roles with commercial use cases rather than purely academic modeling. Enterprise employers account for about 25% of the sample, but small employers also account for about 25%, so this is not just a giant-company market.[23] You can widen your odds by pursuing both enterprise roles and smaller firms that need one person who can query, analyze, explain, and influence.

• Technology product and AI teams (high): Technology employers make up about 30% of the local sample, and these roles often want Python plus machine learning or cloud-adjacent fluency.[5][20]
• Financial services analytics (high): Financial services accounts for about 20% of postings, making it one of the clearest domain targets for candidates who can tie models, dashboards, or analysis to revenue, risk, or operations decisions.[5]
• Healthcare analytics (moderate): Healthcare is about 10% of the sample and tends to reward reporting, KPI definition, and decision support skills, especially as analyst work shifts toward strategic interpretation.[5][19]
• Enterprise BI and data warehouse work (moderate): About 25% of postings come from enterprise employers, and a local Data Warehouse Analyst guide reaches up to $150,000, suggesting solid demand for reporting and warehouse-adjacent work even outside pure AI titles.[23][24]

Where to focus: Target mid-level, business-facing analytics roles in technology, finance, and enterprise operations first, then stretch into AI/ML openings once your portfolio shows decision impact.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appears in about 65% of local postings, making it the clearest baseline language across analyst, data science, and AI work in this market.[20]
• SQL (table stakes): SQL shows up in about 45% of local postings, so weak querying skills will screen you out even when the title sounds model-heavy.[20]
• Machine learning (premium): Machine learning is requested in about 25% of local postings, which makes it a real differentiator once you already clear the Python and SQL bar.[20]
• Data visualization and decision communication (differentiator): Data visualization appears in about 15% of local postings, and the broader role shift is toward interpretation and decision support rather than routine output alone.[20][19]
• AWS and cloud-adjacent analytics (differentiator): AWS appears in about 15% of local postings, which matters because many employers want analytics people who can work closer to production data environments without becoming platform engineers.[20]
• PyTorch and deep learning tooling (premium): PyTorch shows up in about 10% of local postings, so it is most valuable for candidates targeting AI-first or model-building roles rather than general analytics.[20]
• Selective AI certifications (differentiator): Local postings rarely make certifications a hard gate, with 'certified data scientist' appearing in less than 5% of postings, but AI credentials such as CAIP, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer Associate, IBM AI Engineering Professional Certificate, and NVIDIA DLI are gaining prominence as differentiators.[8][7]

Adjacent Roles to Consider

• Business Analyst (bridge): The local skill mix still emphasizes SQL, data analysis, and visualization, which transfers well into business-facing analyst work.[20]
• Revenue Operations Analyst (both): RevOps is visibly active in the local sample, with RevOps Advisor appearing as the most consistently active named employer.[2]
• FP&A or Strategic Finance Analyst (pivot): Financial services accounts for about 20% of the local posting mix, so candidates with modeling and forecasting strength can reposition into finance-heavy analytical work.[5]
• Healthcare Operations Analyst (bridge): Healthcare makes up about 10% of the local sample and rewards KPI definition, reporting, and decision-support skills.[5][19]

30 / 60 / 90-Day Plan

First 30 Days

• Split your search into two lanes: business-facing analytics roles in technology, financial services, and healthcare, and specialized ML or AI roles only where you already have proof of model work.[5]
• Rebuild your resume headline around one clear identity such as BI, analytics, decision science, or applied ML instead of presenting as a broad data generalist.
• Create two portfolio assets: one SQL or dashboard case and one Python analysis or model write-up that ends with a business recommendation.
• Start applying to hybrid and on-site roles, not only remote ones, because remote is only about 15% of the local mix.[4]

Days 31-60

• Target the employer long tail, not just a small brand list; the local sample covers more than 900 companies and is fragmented across employers.[6][1]
• Add one domain story to every interview answer, such as revenue growth, risk reduction, healthcare operations, or executive reporting.
• If you need structured signaling, choose one focused AI or cloud credential rather than stacking generic certificates.[7][8]
• Use community touchpoints such as NYC School of Data to get practitioner feedback, project ideas, and referral leads.[9]

Days 61-90

• Publish a polished case study tailored to the lane generating the most interview traction, and make it easy for recruiters to skim in five minutes.
• Negotiate against market reality: use current posted bands as a directional check, but anchor expectations to level and specialty rather than the highest visible range.[10]
• If interviews stall, widen part of your pipeline into business analyst, RevOps, FP&A, or healthcare operations analyst roles.
• Only add a local program if it produces portfolio work or employer introductions; Manhattan Institute of Management and Transfotech are examples of structured options, but they should support your proof of work, not replace it.[11][12]

Methodology and Confidence

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

Confidence: Overall confidence: Medium. The local unemployment and wage anchors are solid, but several conclusions still rely on category-level and posting-sample inference.

Limitations

• This page mixes very current hiring and labor-market context from May-June 2026 with the most direct metro wage benchmark for data scientists from May 2023, so pay should be read as a historical anchor plus newer posting-based signals rather than one single current market wage.[29][10][30]
• The category spans analyst, BI, data science, machine learning, AI, and operations research work, so salary and demand can vary a lot by specialty even within the same metro.[10][20]
• Statewide New York occupation data was used as a proxy for direction where metro-level monthly occupation data was not available, so Manhattan-heavy hiring trends may not map perfectly to the full New York-Newark-Jersey City region.[14][13]
• 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 than exact counts or percentage shares.[6][2][1][20]
• Several May-June 2026 year-over-year government indicators used here are preliminary and may be revised, so small changes should be treated as directional rather than final.[31][21][15][16][17]

References

1. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
2. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
3. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
4. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
5. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
6. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
7. Ituonline. The Best AI Certifications for IT Professionals in 2026 – ITU Online IT Training · 2026-03 · ituonline.com
8. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
9. Schoolofdata. Get Ready for NYC School of Data 2026! - NYC School of Data · 2026-03 · schoolofdata.nyc
10. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
11. Mimusa. Data Analytics bootcamps: course data analytics in the MIM · 2026-06 · mimusa.edu
12. Transfotechacademy. Data Analytics Bootcamp with Job Guarantee in New York, NY 2026 · 2026-01 · transfotechacademy.com
13. Reveliolabs. Job Openings - Revelio Public Labor Statistics (RPLS) · 2026-06 · reveliolabs.com
14. Reveliolabs. Employment - Revelio Public Labor Statistics (RPLS) · 2026-06 · reveliolabs.com
15. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
16. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
17. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
18. Aituitionhub. How AI Is Transforming Data Science Workflows In 2026 · 2026-05 · aituitionhub.com
19. Simplilearn. Future of Data Analyst: Trends & Career Paths 2026 · 2026-06 · simplilearn.com
20. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
21. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
22. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-04 · data.bls.gov
23. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
24. Robert Half. Staffing, Recruitment & Job Search · 2026-01 · roberthalf.com
25. Reveliolabs. Mass-layoff Notices - Revelio Public Labor Statistics (RPLS) · 2026-06 · reveliolabs.com
26. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
27. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
28. Stlouisfed. Federal Reserve Bank of St. Louis · 2026-07 · stlouisfed.org
29. Bureau of Labor Statistics. Bureau of Labor Statistics - data_scientist_median_annual_wage · 2024-04 · bls.gov
30. Reveliolabs. Salaries - Revelio Public Labor Statistics (RPLS) · 2026-06 · reveliolabs.com
31. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov