San Francisco Data & Analytics: Python, SQL, Machine Learning Lead 1,580 Roles -- December 2025

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San Francisco's data market in December 2025 is defined by AI and machine learning's leading share, with ML Engineers comprising over a third of all openings. The 21:1 senior-to-junior ratio makes this one of the most experience-demanding markets globally, though premium compensation with a $205K median among tracked roles and high remote availability partially offset the competitive entry barriers.

This report analyzes 1,580 Data & Analytics job postings from 680+ 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 36% of roles seeking ML Engineers, investment in production ML skills offers the strongest job security. Focus on Python (53% demand), PyTorch (11%), and MLOps tools like Airflow (11%). The combination of Python + ML skills appears in 8% of all postings.

1.1Leverage Remote for Geographic Arbitrage The 44% remote availability means you can access SF's $205K median compensation from lower cost-of-living areas. Target companies like Roblox, Pinterest, and Reddit that have established distributed work cultures.

1.2Target Mid-Stage Companies for Growth Growth-stage companies (6-15 years old) represent 56% of hiring, offering the best balance of stability, equity upside, and career advancement. These firms often have less rigid seniority requirements than mature enterprises.

1.3Build a Portfolio of Production Systems With only 13% entry accessibility, early-career candidates must demonstrate production experience. Contribute to open-source ML projects, deploy models to production, and document your work to stand out in a market that strongly prefers proven practitioners.

1.4Consider Adjacent Industries While AI & ML companies are obvious targets, Fintech (15%) and Healthcare & Biotech (7%) often have less competitive applicant pools for similar technical work. These industries offer compelling problems and competitive compensation.

Skills Demand

84% of roles with skills data

Low

High

Skills insight: Python leads at 53%, reinforcing its status as the universal language of data and ML. SQL remains essential at 40%, even as the market shifts toward ML - data access and transformation remain foundational. The explicit Machine Learning (21%) and AI (13%) demand reflects role requirements beyond general Python proficiency. PyTorch (11%) outpaces TensorFlow (not in top 15), reflecting the research community's framework preference flowing into production. LLMs appearing at 9% represents the generative AI wave hitting job requirements. The dbt (9%) + Snowflake (9%) combination indicates modern data stack adoption. C++ at 7% reflects performance-critical ML systems, particularly in autonomous vehicles and real-time applications. The Python + SQL pairing (31%) remains the most fundamental skill combination.

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

21:1

Senior+ roles per Junior role

Entry Accessibility Rate

13%

Junior + Mid-Level roles combined

The 21:1 senior-to-junior ratio creates one of the most experience-demanding markets globally. Senior roles (58%) lead, with Staff/Principal (22%) representing strong demand for technical leaders who can architect complex systems and mentor teams. The 4% junior allocation suggests that SF employers largely expect to hire trained professionals rather than develop them - a market dynamic that pushes early-career talent toward other cities or industries. Mid-level positions (9%) offer a narrow pathway between entry and senior levels. Director-plus roles (7%) indicate healthy leadership opportunities for those who advance through the technical ranks. 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

56% of roles with known working arrangement

44%Remote

Remote44%

Onsite24%

Hybrid23%

Flexible9%

Remote work leads at 44%, making SF's premium compensation accessible from anywhere - though this also means global competition for positions. Only 24% of roles require full-time office presence, typically for positions involving sensitive data, hardware integration, or real-time collaboration needs. The combined 76% flexibility rate (remote + hybrid + flexible) reflects the tech industry's post-pandemic normalization of distributed work. Companies like Waymo and Zoox that require physical presence for vehicle testing likely account for much of the onsite share. Hybrid arrangements (23%) often translate to 2-3 days per week in office, concentrated in SF's SOMA and Mission districts.

Role Specialization

Low

High

The 36% ML Engineer concentration is a notable characteristic of SF's data market - more than triple the Data Analyst share (7%). This reflects the Bay Area's shift from analytics-driven decision making to AI-powered product development. Combined with Data Scientists (20%) and Research Scientists (3%), ML-focused roles represent 59% of the market. Data Engineers (19%) remain critical for building the pipelines that feed ML systems. The relatively small Analytics Engineer (4%) and Data Analyst (7%) segments suggest that traditional BI and reporting work increasingly flows to other markets or becomes automated. Product Analytics (6%) maintains relevance as the bridge between data teams and product decisions.

IC vs Management Track

92%IC

Individual Contributor92%

Management8%

The 92% IC concentration reflects the technical nature of SF's data work and the industry's preference for flat organizational structures. With complex ML systems requiring deep individual expertise, companies invest heavily in technical tracks over management hierarchies. The 8% management share includes data team leads, analytics managers, and directors of data science. This ratio suggests that career advancement in SF's data market flows primarily through technical excellence (Staff/Principal track) rather than people management. Candidates seeking management careers may find more opportunity in enterprise-focused markets or industries with larger, more traditional organizational structures.

Compensation

34% of roles with disclosed salary ranges

Overall Distribution

25th Percentile

$172K

Median

$205K

75th Percentile

$250K

IQR (Spread)

$78K

Advertised Salary by Seniority

Advertised Salary by Role

Market Context

1.AI Investment Growth Drives ML Demand Strong venture capital investment in AI startups throughout 2025 has created strong demand for ML Engineers. Companies across sectors are building AI capabilities, with SF-based employers at the forefront. This investment cycle particularly favors candidates with production ML and LLM experience.

2.Autonomous Vehicle Sector Expands Waymo and Zoox represent the largest single-company hiring clusters in the market. The AV industry requires massive data teams for perception, mapping, prediction, and simulation. This sector offers some of the most technically challenging work in the field, with compensation to match.

3.Pay Transparency Improves Market Visibility Among tracked roles, 34% disclose salary ranges based on employer-provided data from direct postings. This lower coverage likely reflects the exclusion of predicted or estimated salaries, limiting figures to verified employer disclosures. The $77,912 interquartile range reveals wide compensation variation based on role and company.

4.Remote Work Enables Geographic Competition With 44% remote availability, SF compensation is accessible globally. This creates opportunity for candidates outside the Bay Area but also intensifies competition for each role. Employers benefit from expanded talent pools while candidates face a more competitive landscape.

5.Seniority Skew Creates Development Gap The 21:1 senior-to-junior ratio suggests a market-wide underinvestment in talent development. As senior professionals age out of the workforce, companies may face acute shortages without pipeline investment. Early movers in developing junior talent may gain long-term competitive advantage.

Methodology

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

Data collection:

1.Over 1,500 roles from 680+ employers aggregated from multiple sources
2.Recruitment agency postings identified and excluded (1% 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 1,331 roles with skill data (84% coverage)
3.Salary data covers 34% of tracked roles, limited to employer-disclosed ranges from direct postings
4.Working arrangement specified in 56% of postings

Data coverage:

85%

Seniority coverage

Roles with seniority level classified

56%

Arrangement coverage

Roles with working arrangement known

84%

Skills coverage

Roles with skills extracted from description

77%

Employer metadata

Roles with enriched company data

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