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 Hiring Managers

1.0Prepare for Compensation Pressure With a $205K median among the 34% of tracked roles disclosing salary ranges and Research Scientists reaching $251K, salary expectations are calibrated to SF market rates regardless of your company's location. Remote policies extend your talent pool but also your competition for candidates.

1.1Reconsider Seniority Requirements The 21:1 senior-to-junior ratio indicates industry-wide reluctance to invest in developing talent. Companies willing to hire and develop mid-level candidates (9% of current market) can build loyal teams while competitors fight over the same senior pool.

1.2Differentiate on Mission and Impact With 680 employers competing for similar talent, compensation alone won't win candidates. Top employers like Waymo and OpenAI attract talent through compelling technical challenges. Articulate your data team's impact on business outcomes.

1.3Invest in Staff/Principal Roles Strategically Staff/Principal positions (22% of market) reach $248K median compensation among tracked roles. These senior ICs drive technical direction and mentor growing teams. Ensure you have meaningful scope and influence to offer, not just inflated titles.

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

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: Waymo's market-leading 3% share reflects the data intensity of autonomous vehicle development - perception, prediction, and planning systems require massive ML teams. The presence of two AV companies (Waymo, Zoox) in the top 10 underscores this sector's talent appetite. OpenAI's inclusion underscores SF's role as the center of the generative AI revolution. The top 15 employers among tracked direct employer platforms collectively account for 23% of hiring, with mid-size companies offering substantial opportunity. Fintech representation (Capital One, Block, Plaid, Airwallex) demonstrates that financial services remain a consistent source of data roles.

Industry Distribution

77% of roles with industry data

Low

High

Fintech leads at 15%, reflecting San Francisco's concentration of financial technology companies from established players like Block and Plaid to emerging challengers. The combined AI/ML (12%) and Data Infrastructure (6%) segments total 18%, underscoring how data tooling and AI development have become industries in their own right. Mobility & Transportation (11%) is driven primarily by autonomous vehicle companies like Waymo and Zoox, which require massive data teams for perception, prediction, and decision-making systems. The relatively modest Healthcare & Biotech share (7%) suggests SF's data talent flows primarily toward consumer and enterprise technology.

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.

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.

Company Maturity

76% of roles with company age data

56%Growth Stage

Growth (6-15 yrs)56%

Mature (>15 yrs)30%

Young (<=5 yrs)14%

Growth-stage companies founded between 2010-2019 lead hiring at 56%, representing the startup cohort that scaled successfully through the 2010s tech boom. These include companies like Databricks, Roblox, and Instacart that now employ thousands but retain startup DNA and equity upside. Mature companies (30%) provide stability and often larger data platforms to work with. Young startups (14%) offer the highest risk-reward profile - smaller teams mean more ownership but less job security. This distribution suggests the SF data market has matured past its founding generation, with mid-stage companies now driving the majority of hiring.

Ownership Type

76% of roles with ownership data

61%Private

Private62%

Public29%

Subsidiary9%

Acquired0%

Private companies represent 61% of hiring, reflecting the Bay Area's venture-backed ecosystem. Many of these are late-stage privates like Databricks, OpenAI, and Stripe that offer competitive compensation while retaining equity upside potential. Public companies (29%) include household names like Pinterest, Reddit, and Roblox that provide liquidity and stability. The minimal acquired company presence (0%) suggests that data teams at acquired companies either integrate into parent organizations or experience headcount reductions. The 9% subsidiary share includes companies like Capital One's tech operations that benefit from corporate backing while maintaining startup-like cultures.

Employer Size Distribution

76% of roles with company size data

39%Enterprise

Enterprise (1,000+)39%

Scale-up (50-1,000)33%

Startup (<50)28%

The relatively even distribution across company sizes - 39% Enterprise, 33% Scale-up, 28% Startup - gives candidates meaningful choice in work environment. Enterprise employers offer established data platforms, clear career ladders, and comprehensive benefits. Scale-ups provide the sweet spot of meaningful ownership with reduced early-stage risk. Startups demand generalist capabilities but offer outsized impact and equity potential. Notably, the startup share (28%) remains substantial despite the challenging funding environment, indicating continued investment in data capabilities even among smaller companies.

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.

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.

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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