New York Data & Analytics: SQL, Python, Machine Learning Lead 2,231 Roles -- December 2025

Beta

New York's data market remains active with 2,231 open roles across 972 employers, driven by steady demand for ML engineering talent. Among tracked roles, 21% disclose salary ranges, reflecting only employer-disclosed salary data from direct postings.

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.

Key Takeaways for 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.

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.

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.

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.

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.

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.

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

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.

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

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