New York Data & Analytics Market

Beta

December 2025

This report analyzes 2,231 Data & Analytics job postings from 972+ companies tracked via direct employer career pages and job board aggregators. Our coverage skews toward tech-forward and scaling companies; large enterprises using enterprise hiring platforms may be underrepresented. Coverage varies by section and is noted throughout.

Market Context

1.New York Pay Transparency Law Impact Since November 2022, New York City requires salary ranges on job postings. In this report, 21% of tracked roles include disclosed salary ranges, reflecting only employer-disclosed salary data from direct postings. The lower coverage compared to the legal requirement likely reflects variation in how employers comply across different posting channels.

2.Financial Services AI Shift Wall Street's aggressive adoption of AI and ML is driving the 21% combined demand from Fintech and Financial Services. Capital One's 5% market share and JPMorgan's combined 4% reflect major investments in data capabilities for risk, trading, and customer analytics.

3.ML Engineering Growth The shift from Data Scientist to ML Engineer as the top role (27% vs 19%) suggests industry maturation. Companies are moving beyond proof-of-concept ML to production systems requiring engineering rigor, infrastructure knowledge, and MLOps capabilities.

4.Remote Work Persistence in Data Despite broader return-to-office mandates, 87% of data roles maintain flexibility. This reflects both the technical nature of data work (which translates well to remote) and competitive pressure - companies requiring full onsite presence struggle to attract talent at equivalent compensation levels.

5.Entry-Level Talent Pipeline Challenge The 12:1 senior-to-junior ratio and 20% entry accessibility rate indicate a structural challenge: companies prefer experienced hires, creating potential long-term talent pipeline issues. Bootcamps and academic programs continue to produce graduates, but absorption into the industry remains bottlenecked.

Key Takeaways: Job Seekers

1.0Prioritize ML Engineering Skills With 27% of roles focused on ML Engineering and a median salary of $229,500 among those with disclosed pay, investing in production ML skills (PyTorch, model deployment, MLOps) likely offers a strong return. The gap between ML Engineer and Data Analyst compensation ($229,500 vs $147,800 median) is notable among tracked roles with salary data.

1.1Master the Python + SQL Foundation These two skills appear in 38% of postings each, with 23% of roles requiring both together. This combination forms the baseline expectation - pair it with Snowflake (10%), dbt (8%), or Airflow (8%) to stand out.

1.2Look Beyond the Big Names While Capital One and JPMorgan lead headlines, our tracked employers include a wide range of companies across industries and sizes. Exploring less prominent names among the 972 employers in our dataset can surface roles with comparable compensation and potentially less competition.

1.3Leverage Remote Opportunities 46% of roles with disclosed arrangements offer fully remote work. This is particularly valuable for candidates outside Manhattan who can access NYC-level salaries ($192,500 median among disclosed ranges) without the commute or relocation costs.

1.4Consider Entry-Level Strategy Carefully The 12:1 senior-to-junior ratio means intense competition for entry-level roles. Consider targeting scale-ups (32% of market) rather than enterprises, or pursuing Product Analytics (7% of roles) which may have less competition than pure data science tracks.

Key Takeaways: Hiring Managers

2.0Benchmark Compensation Carefully Among the 21% of tracked roles with disclosed salary ranges, ML Engineers show a $190K-$260K range (25th-75th percentile). The $192,500 overall median among disclosed ranges may serve as a useful starting point for budget planning, though the limited disclosure rate means the full market picture is not captured.

2.1Differentiate Through Flexibility While 87% of the market offers some flexibility, only 46% are fully remote. If you can offer true location independence, emphasize it prominently - it remains a key differentiator, especially for senior talent at Staff/Principal levels ($249,000 median among disclosed ranges).

2.2Address the ML Talent Shortage ML Engineers represent 27% of demand and earn premium salaries. Consider building internal training pipelines from your Data Scientists and Data Engineers, or look at candidates from the 72 Research Scientist roles who may want more applied work.

2.3Compete on Company Story, Not Just Pay With 972 tracked employers posting data roles, employer brand matters. Highlight growth trajectory, technical challenges, and team composition - 48% of roles are at enterprises, so nimble scale-ups can differentiate on impact and ownership.

Industry Distribution

58% of roles with industry data

Low

High

New York's industry mix reflects its dual identity as a financial capital and consumer tech hub. Finance-adjacent sectors (Fintech + Financial Services) account for 21% of demand, led by major players like Capital One and JPMorgan. Consumer Tech leads at 13%, driven by companies like Pinterest, Reddit, and Instacart. The presence of Healthcare & Biotech (8%) and AI/ML pure-plays (7%) indicates diversification beyond traditional Wall Street data roles.

Company Maturity

55% of roles with company age data

48%Growth Stage

Growth (6-15 yrs)48%

Mature (>15 yrs)41%

Young (<=5 yrs)11%

The market skews heavily toward established companies, with 89% of roles at companies older than 5 years. Growth-stage companies (6-15 years) lead at 48%, representing the wave of startups founded 2010-2019 that have scaled into major employers. Mature companies (>15 years) at 41% reflect New York's established financial and media industries. Young startups (11%) offer higher risk/reward opportunities but represent a smaller slice of the market.

Ownership Type

56% of roles with ownership data

57%Private

Private56%

Public35%

Subsidiary8%

Acquired1%

Private companies lead at 57%, offering potential equity upside for candidates comfortable with liquidity risk. Public companies represent 35% of roles, providing stability, transparent compensation via SEC filings, and typically stronger benefits. The subsidiary segment (8%) often includes divisions of large financial institutions operating as distinct entities. Recently acquired companies (1%) may offer integration opportunities or transition risk depending on timing.

Employer Size Distribution

58% of roles with company size data

48%Enterprise

Enterprise (1,000+)48%

Scale-up (50-1,000)32%

Startup (<50)20%

Nearly half (48%) of roles are at enterprises with 1,000+ employees, reflecting New York's concentration of large financial institutions and established tech companies. Scale-ups (32%) offer a middle ground with established revenue but continued growth potential. Startups at 20% provide higher ownership and impact potential, though typically with less structured data infrastructure - appealing to builders who want to shape data strategy from the ground up.

Employers Hiring for Data & Analytics Roles

Large employers with proprietary career sites (Amazon, Google, Meta, Microsoft) may be underrepresented, as our sources primarily capture roles posted through standard direct employer platforms and job aggregators. We are actively expanding our integrations.

Low

High

Market interpretation: Capital One leads with 5% market share, followed by JPMorgan Chase at 4%. The mix includes consumer tech (Pinterest, Reddit, Instacart), AI-focused companies (Scale AI, Datadog), professional services (EY), and quantitative finance (Point72). This diversity across finance, tech, and AI reflects New York's position at the intersection of these industries.

Role Specialization

Low

High

ML Engineer roles lead at 27%, ahead of traditional Data Scientist positions (19%), suggesting a maturation of the market - companies are moving from experimental ML to production systems. Data Engineering (18%) remains foundational, while Analytics Engineering (4%) as a distinct category reflects the modern data stack's influence. Research Scientists in ML (4%) show the highest median salaries among tracked roles with disclosed pay but represent specialized positions at companies pushing algorithmic boundaries.

Seniority Distribution

Junior: 0-2 years | Mid-Level: 3-5 years | Senior: 6-10 years | Staff/Principal: 11+ years (IC track) | Director+: Management track

Low

High

Senior-to-Junior Ratio

12:1

Senior+ roles per Junior role

Entry Accessibility Rate

20%

Junior + Mid-Level roles combined

Senior roles lead at 53%, with Staff/Principal (16%) and Director+ (11%) adding another 27% at the top. This creates a 12:1 senior-to-junior ratio, making entry challenging. Only 20% of roles are accessible to candidates with under 3 years experience (Junior 6% + Mid-Level 14%). The concentration at senior levels reflects both market maturity and the specialized nature of data work - companies prefer to hire experienced practitioners who can deliver immediate impact. Note: The ratio reflects all senior-level roles (Senior + Staff/Principal + Director+) divided by Junior positions.

IC vs Management Track

88%IC

Individual Contributor88%

Management12%

The 88% IC concentration reflects the technical nature of data roles and the industry's preference for deep specialists. Management positions (12%) align with Director+ representation, suggesting most leadership roles combine people management with technical oversight. This structure offers clear IC career paths to Staff/Principal levels without requiring a management transition, appealing to technically-focused professionals.

Working Arrangement

Onsite: office full-time | Hybrid: mix of office and remote | Remote: work from anywhere | Flexible: employee chooses arrangement

38% of roles with known working arrangement

46%Remote

Remote46%

Hybrid30%

Onsite13%

Flexible12%

Among roles with disclosed arrangements, remote work leads at 46%, followed by hybrid (30%). Only 13% require full onsite presence, a notable contrast to broader return-to-office trends. The 12% flexible category typically indicates employee choice between remote and hybrid. Combined, 87% of roles offer some form of location flexibility, making New York's data market accessible to candidates who prefer or require non-traditional arrangements.

Compensation

21% of roles with disclosed salary ranges

Overall Distribution

25th Percentile

$163K

Median

$193K

75th Percentile

$230K

IQR (Spread)

$67K

Advertised Salary by Seniority

Advertised Salary by Role

Skills Demand

70% of roles with skills data

Low

High

Skills insight: Python and SQL tie as the most demanded skills at 38% each, forming the non-negotiable foundation. Machine Learning (21%) and AI (15%) reflect the market's ML Engineering emphasis. The modern data stack is evident: Snowflake (10%), dbt (8%), and Airflow (8%) appear frequently. For ML specialists, PyTorch (7%) edges out TensorFlow (5%), and LLMs (5%) are emerging as a distinct requirement. AWS (10%) leads cloud platforms, with GCP (6%) and Azure (5%) as alternatives.

Market Metrics

Market Structure

2.3

Jobs per employer

Average open roles per hiring company

Among tracked employers

11%

Top 5 concentration

Combined market share of top 5 employers

Among tracked employers

19%

Top 15 concentration

Combined market share of top 15 employers

Among tracked employers

Accessibility

12:1

Senior-to-Junior ratio

Senior+ roles for every junior role

Very competitive entry

20%

Entry accessibility

Junior + Mid-Level roles combined (typically <3 years experience required)

Competitive entry market

12%

Management opportunity

Roles on the people management track

IC-focused

Flexibility

46%

Remote availability

Roles offering fully remote work

High flexibility

Methodology

This report analyzes direct employer job postings for Data & Analytics roles in New York during December 2025.

Data collection:

1.Over 2,200 roles from 972+ employers aggregated from multiple sources
2.Recruitment agency postings identified and excluded (6% of raw data)
3.Jobs deduplicated across sources to avoid double-counting

Classification:

1.Roles classified using an LLM-powered taxonomy
2.Subfamily, seniority, skills, and working arrangement extracted
3.Employer metadata enriched from company databases where available

Limitations:

1.Not a complete census of the market - some roles may not be captured
2.Skills analysis based on 1,560 roles with skill data (70% coverage)
3.Salary data limited to 21% of roles with employer-disclosed ranges from direct postings; predicted or estimated salaries excluded
4.Working arrangement specified in 38% of postings

Data coverage:

87%

Seniority coverage

Roles with seniority level classified

38%

Arrangement coverage

Roles with working arrangement known

70%

Skills coverage

Roles with skills extracted from description

59%

Employer metadata

Roles with enriched company data

About

This report was created by Rich Jacobs, Data product manager focused on hiring market intelligence. Want the data? rich@richjacobs.me