If you are a UK or European company building a US data science or AI team in 2026, the question of where to hire matters as much as what to pay. The US data and AI talent market is not evenly distributed.
It clusters in specific cities, within specific industries, at salary levels that vary significantly by location. Getting the geography wrong adds cost, extends timelines, and can leave you competing for talent in markets where you will consistently lose to employers with stronger local brand recognition.
This guide covers where the US data science, analytics and AI hiring market actually is in 2026, what the verified salary data shows by city, what each major market is best for, and what UK and European companies need to understand before making their first US data hire.
The State of US Data Science Hiring in 2026
The AI hiring boom of 2024 and 2025 has reshaped the US data talent market in ways that are still playing out.
LinkedIn’s 2026 Jobs on the Rise report identified AI Engineer as the fastest-growing job title in the United States, with postings up 143% year-over-year in 2025. This is not a niche movement. Demand for AI and data science professionals has crossed from technology companies into financial services, healthcare, retail, logistics, and professional services.
According to Axial Search’s 2026 analysis of 12,148 US data science job postings, the market runs at approximately 828 new US data science postings per week. This is substantial, consistent volume rather than a frenzy. Notably, 92% of those postings are for permanent full-time roles, not contract positions. Companies are building data science as a permanent capability.
CBRE’s Scoring Tech Talent 2025 report, cited by KORE1, found that the pool of workers with AI skills jumped more than 50% in a single year, reaching approximately 517,000 nationally. But this talent is not evenly distributed. It clusters. And where it clusters is changing faster than most international employers realise.
The Bureau of Labor Statistics projects 34% job growth in data science roles from 2024 to 2034, significantly outpacing most professions. For international companies planning US team builds over multiple years, this trajectory matters for headcount planning and salary budgeting.
According to 365 Data Science’s 2026 analysis, remote data science positions now account for approximately 12% of US data science roles, while hybrid arrangements make up over 50% of US positions. This matters for international employers: a meaningful share of the US data science market can be hired remotely, which opens access to lower-cost talent markets while still building real US market presence.
What Verified Salary Data Shows for 2026
Before covering individual cities, the headline national picture.
According to Indeed’s data from July 2026, based on 6,700 verified salary submissions, the average US data scientist salary is $131,179 per year. The range is wide: $80,510 at the 25th percentile, $213,737 at the 90th percentile.
KORE1’s 2026 data scientist salary guide notes that mid-level data scientists nationally earn between approximately $138,000 and $175,000 annually, with senior data scientists from $157,000 to $194,000. Production specialisations, particularly in LLM engineering, generative AI, and causal inference, add $30,000 to $60,000 over generalist roles.
For international employers, these numbers need to be read alongside the benefits cost. Health insurance, 401(k) matching, workers’ compensation and other employer obligations add approximately 20% to 25% on top of base salary in total employer cost. A $150,000 data scientist carries a total employer cost of approximately $180,000 to $190,000 per year before equipment, software, and management overhead.
The Major US Data Science Hiring Markets
San Francisco Bay Area
What it is: The highest-compensation, highest-competition data science market in the United States.
San Francisco remains the centre of the US AI and data science ecosystem, home to OpenAI, Anthropic, Google DeepMind, Salesforce, Meta, and hundreds of AI-native startups. The talent density here is unmatched anywhere in the world.
Salary data (verified 2026):
- Indeed July 2026: average data scientist salary in San Francisco $172,324 per year
- KORE1 2026: average base salary approximately $172,345, roughly 30% above the national figure
- Motion Recruit 2026: mid-level data scientists earning $155,000 to $218,000; senior roles up to $218,000
What this market is for:
- Frontier AI and machine learning research
- Generative AI and LLM engineering
- Companies where cutting-edge AI capability is the core product
- Hiring researchers and engineers who work at the boundary of what is commercially deployed
What international employers need to know: San Francisco is where you hire if you are building something that competes with OpenAI and Anthropic for talent. It is not where you hire if you need solid applied data science for business intelligence, customer analytics, or operational modelling. The talent is there but the cost premium is significant and the competition is fierce. Time-to-hire for strong candidates is fast. KORE1 reports their desk averages 17 days to hire. A slow or complex hiring process at this level will cost you candidates consistently.
New York City
What it is: The largest volume market for data science hiring in the US, with the widest industry spread.
CoworkingCafe data cited by Research.com showed New York added over 15,000 new AI job postings between late 2024 and the end of 2025, more than any other US city. Axial Search’s 2026 analysis confirms Seattle and San Francisco are nearly tied for top hiring volume, with New York, Chicago, Boston, and Atlanta all in the top five.
Salary data (verified 2026):
- Glassdoor May 2026: average data scientist salary in New York City $168,822 per year; range $130,076 to $221,871
- Motion Recruit 2026: mid-level New York data scientists earning $155,000 to $196,000; senior roles up to $218,000
What this market is for:
- Financial services, investment banking, hedge funds, and fintech. New York is the dominant market for quantitative analytics and data science applied to financial data
- Media, advertising technology, and marketing analytics
- Management consulting and professional services firms building data capabilities
- Large enterprise technology buyers with significant data infrastructure
What international employers need to know: New York is the best market for UK and European financial services companies, fintech firms, and professional services companies making their first US data hire. The candidate pool is deep, the industry context is familiar if you are coming from the City of London or European financial centres, and the salary premium versus national averages is real but manageable.
Seattle
What it is: The cloud and enterprise software capital of the US data market.
Home to Amazon Web Services and Microsoft Azure, the two platforms that underpin most of the world’s enterprise data infrastructure. Seattle has a data science talent pool shaped by two of the most technically demanding employers in the world.
Salary data (verified 2026):
- Indeed July 2026: average data scientist salary in Seattle $155,659 per year
- Motion Recruit 2026: mid-level Seattle data scientists earning $148,000 to $187,000; senior roles up to $208,000
- Axial Search 2026: Seattle and San Francisco nearly tied as the top two US data science hiring cities by volume
What this market is for:
- Cloud data engineering, data platform, and data infrastructure roles
- Machine learning engineering applied to large-scale systems
- E-commerce, logistics, and supply chain analytics
- Companies building on AWS or Azure who want engineers with deep platform knowledge
What international employers need to know: Seattle is often underestimated by European companies who default to New York or San Francisco. For data engineering, MLOps, and cloud-native analytics roles, Seattle has a talent depth that is hard to match elsewhere. The Amazon and Microsoft alumni networks produce engineers who have operated data systems at a scale that few companies anywhere in the world can match.
Note on Seattle salaries: the Indeed figure of $155,659 covers all data scientist roles. SignalHire’s 2026 analysis notes that Seattle ML professionals specifically command a median of approximately $197,400 per year according to Glassdoor, reflecting the premium for machine learning specialists at Amazon and Microsoft. Budget accordingly if your hire is ML-focused rather than a generalist data scientist.
Boston
What it is: The leading US market for data science applied to life sciences, healthcare, and academic research.
Harvard, MIT, and a cluster of world-class research hospitals make Boston the most research-intensive data science market in the US. Kendall Square in Cambridge is home to some of the highest concentrations of life sciences and biotech data science roles anywhere in the world.
Salary data (verified 2026):
- Motion Recruit 2026: mid-level Boston data scientists earning $141,000 to $179,000; senior roles $161,000 to $200,000
- KORE1 2026: remote roles run second nationally at $159,290, ahead of New York, Seattle, and Boston
What this market is for:
- Life sciences, biotech, and pharmaceutical data science and bioinformatics
- Healthcare analytics and health technology
- Clinical data and regulatory affairs data roles
- Academic-to-industry transition roles for researchers moving into applied positions
What international employers need to know: If your company is in life sciences, medtech, or health technology, Boston is often a better first US data hire location than New York or San Francisco. The talent pool has research rigour that is hard to find elsewhere, and the life sciences ecosystem is dense enough that your company will be recognised by candidates even if your US brand is not yet established.
Chicago
What it is: The most underestimated major US data science market, with particular strength in enterprise analytics and financial technology.
Chicago is the third-largest tech market in the US by employment, a fact that surprises many European companies who default to coastal hubs. It has deep talent in financial technology, enterprise analytics, logistics, and supply chain data roles, with lower average salaries and cost of living than New York or San Francisco.
What this market is for:
- Financial technology, options and derivatives analytics
- Enterprise software and business intelligence
- Logistics, supply chain, and operations research
- Large-scale B2B analytics for enterprise clients
What international employers need to know: Chicago delivers strong analytical talent at lower cost than the coastal markets. For international companies that need solid applied data science without the premium of New York or San Francisco, Chicago is worth serious consideration. The talent pool is large enough to support meaningful team growth, and the cost of living advantage helps with retention.
The Emerging Tier: Raleigh-Durham, Austin, Atlanta, Denver
These markets are where the growth is happening fastest in 2026.
KORE1’s emerging tech hubs 2026 analysis notes that Raleigh-Durham has been tagged by CBRE as the most improved US tech talent market and has moved to number 12 overall in CBRE’s 2025 ranking. Austin has graduated from the emerging tier to a top-five global tech talent market. Atlanta is rapidly building strength in AI and cloud. Denver has strong cybersecurity, cloud infrastructure, and data analytics talent at more manageable cost than coastal markets.
Axial Search’s 2026 data shows Chicago, Boston, and Atlanta all ranking in the top five US cities for data science hiring volume.
These markets offer three advantages for international companies building US teams:
- Lower salary expectations than San Francisco, New York, or Seattle
- Significantly lower cost of living, which helps with employee retention and quality of life
- Growing but not yet saturated talent pools where an international employer’s brand can stand out more easily
The risk: the talent pool is thinner at the senior and specialist end. For roles that require deep ML research experience or frontier AI credentials, the coastal markets still have significant depth advantages.
US Data Science Hiring Markets: Side-by-Side Comparison
The table below draws together the key data points for each market to help you evaluate them side by side. Salary figures are from verified 2026 sources. Industry strengths and talent depth ratings reflect market analysis from CBRE Scoring Tech Talent 2025, Axial Search 2026, and KORE1 2026.
Market | Avg data scientist salary (2026) | Primary industry strengths | Talent depth (frontier AI) | Talent depth (applied analytics) | Relative cost vs national avg | Best for |
San Francisco Bay Area | $172,324 (Indeed) | Frontier AI, LLM engineering, cloud platforms | Very high | High | +31% | Frontier AI, generative AI, research roles |
New York City | $168,822 (Glassdoor) | Financial services, fintech, media, consulting | High | Very high | +29% | FinTech, finance, media, marketing analytics |
Seattle | $155,659 (Indeed) | Cloud infrastructure, e-commerce, ML Ops | High | High | +19% | Cloud data engineering, platform roles, MLOps |
Boston | $141,000-$179,000 (Motion Recruit) | Life sciences, biotech, healthcare, academia | Medium-High | High | +8-17% | Pharma, biotech, health tech, research roles |
Chicago | $138,000-$165,000 (market range) | Enterprise analytics, fintech, logistics | Medium | High | At or below avg | Enterprise BI, B2B analytics, cost-efficient hiring |
Austin | $140,000-$160,000 (market range) | Tech startups, SaaS, semiconductor | Medium | Medium-High | Below avg | Early-stage teams, startup hiring, lower burn |
Raleigh-Durham | $120,000-$150,000 (market range) | Life sciences, enterprise tech, research | Medium | Medium-High | Well below avg | Cost-efficient hiring, biotech, strong universities |
Atlanta | $130,000-$155,000 (market range) | Fintech, cloud, enterprise tech | Medium | Medium-High | Well below avg | Growing tech ecosystem, diverse talent, fintech |
Denver | $130,000-$155,000 (market range) | Cybersecurity, cloud, data analytics | Medium | Medium | Below avg | Remote-friendly teams, work-life balance hires |
How to read this table: “Talent depth” indicates how thick the specialist candidate pool is in that market. A High or Very High rating in frontier AI means you will find research-grade ML talent. Medium means applied data science is well-served but frontier researchers are scarcer. All salaries are base only; add 20-25% for total employer cost including health insurance, FICA, 401(k), and workers’ compensation. San Francisco, New York, and Seattle salary figures are from Indeed and Glassdoor primary data (July/May 2026). Boston figures are from Motion Recruit 2026. Chicago, Austin, Raleigh, Atlanta, and Denver are shown as market ranges reflecting multiple 2026 data sources and should be treated as indicative rather than precise averages.
What This Means for International Companies Building US Data Teams
Hire for the Role, Not the Brand Name
The instinct of many European companies is to hire in San Francisco or New York because those are the names they know. That is the right answer for some roles and the wrong answer for many others.
Match the role to the market:
- Frontier AI / LLM research: San Francisco or Seattle. The talent does not concentrate elsewhere at the research level.
- Financial data science, quant analytics, fintech: New York. No other US market matches the depth of financial services data talent.
- Cloud data engineering, MLOps, platform roles: Seattle. The AWS and Microsoft Azure alumni networks are unmatched here.
- Life sciences, biotech, clinical data: Boston. Harvard, MIT, and the Route 128 biotech corridor create a research talent pool found nowhere else at this density.
- Applied analytics, enterprise BI, cost-efficient hiring: Chicago, Atlanta, Raleigh-Durham, or Denver. Strong talent, lower salary expectations, and lower cost of living improve retention.
- First US data hire on a startup budget: Austin, Raleigh-Durham, or Atlanta. Top-tier talent at costs that work before you have Series B economics.
A data analyst building business intelligence dashboards does not need to be in San Francisco and will cost you significantly less elsewhere. A machine learning engineer building production LLM systems may well need to be in a market where that specialist talent concentrates.
Plan for total employer cost, not just salary
Salary is the starting point. The employer payroll tax, health insurance obligation, 401(k) match, workers’ compensation, and other employment costs add approximately 20% to 25% to the base salary in total employer cost. For a $170,000 data scientist in New York, budget approximately $200,000 to $215,000 in total employer cost before equipment and management overhead.
Our guide to budgeting for your first US hire provides a full cost model with verified 2025/2026 figures. And our US salary benchmarking guide covers how to set compensation that is competitive in your specific target market.
The EOR and State Registration Question
Wherever you hire, you need to be legally registered to employ in that state. The compliance picture changes significantly depending on which market you choose.
State-specific complexity by market:
- California (San Francisco): Among the most complex US employment states. Mandatory SDI contributions, strict final pay laws, expansive anti-discrimination protections, and the most employee-friendly classification rules in the country.
- New York: State PFL (Paid Family Leave) mandatory. NYC has additional local laws on pay transparency, fair chance hiring, and predictive scheduling.
- Washington (Seattle): No state income tax, which simplifies payroll. State-level paid family and medical leave applies.
- Massachusetts (Boston): Specific restrictions on non-compete agreements. PFML contributions mandatory.
- Texas, Florida (Austin, Miami): No state income tax. Lower regulatory complexity. Generally more business-friendly employment environment.
Through an Employer of Record, your data scientist can be on payroll and fully compliant in their state within days of your decision to hire, without navigating state-specific registrations independently. As your team grows into multiple states, the EOR manages each state’s compliance calendar on your behalf.
Our guide to how employer of record works explains the model in full. And our smart tech talent hotspots guide covers the broader picture of where US tech talent is concentrating in 2026 beyond the established coastal markets.
Speed Matters More Than You Think
KORE1 reports their average time-to-hire for data science roles is 17 days. Strong data science and AI candidates in the major US markets are typically off the market within three to four weeks of beginning their search.
What slows international companies down and costs them candidates:
- Too many interview rounds. Three to four rounds is the US market norm. Five or more signals indecision and loses candidates who have competing offers.
- Long gaps between stages. If a candidate submits a take-home assessment and hears nothing for two weeks, they have already accepted another offer.
- Offer approval requiring sign-off from a European head office in a different timezone. Build the approval process before you start the search, not after you want to make an offer.
- Equity or compensation packages that do not meet US market expectations. A data scientist who has been competing for roles at Google and Stripe will not accept a package calibrated to UK market rates.
- Unfamiliarity with US visa sponsorship questions. Many strong candidates are on OPT or H-1B sponsorship timelines. Knowing your position on this before the first interview avoids late-stage dropouts.
An international company running a slow process consistently loses candidates to US employers who move faster. Build the decision timeline before you post the role.
Foothold America’s Talent Acquisition service works with international companies on US data and technology hires. We understand the US data science market, the salary benchmarks by city and role, and the speed at which strong candidates move. If you are planning a US data team build, speak to our team before you start the search.
Frequently Asked Questions: US Expansion
Get answers to all your questions and take the first step towards a US business expansion.
It depends on the role. San Francisco leads for frontier AI talent. New York has the highest hiring volume and the best financial services data talent. Seattle leads for cloud and infrastructure roles. Boston is best for life sciences. Chicago, Atlanta, and Raleigh-Durham offer strong talent at lower cost.
According to Indeed’s July 2026 data based on 6,700 salary submissions, the average US data scientist salary is $131,179 per year. Salaries range from approximately $80,510 at the 25th percentile to $213,737 at the 90th percentile. Location, specialisation, and employer type all significantly affect where in that range a hire falls.
According to Indeed’s July 2026 data, the average data scientist salary in San Francisco is $172,324 per year. KORE1’s 2026 salary guide puts the average base at approximately $172,345, roughly 30% above the national average. Senior and specialised roles, particularly in generative AI and causal inference, earn significantly more.
Strong data science and AI candidates in major US markets typically receive offers within two to three weeks of starting their search. KORE1 reports an average time-to-hire of 17 days for data science roles. International companies with slow interview and decision processes consistently lose candidates to faster-moving US employers.
For many applied data science and analytics roles, yes. These markets offer strong talent at lower salaries and significantly lower cost of living, which helps with retention. For frontier AI research or production LLM engineering, the major coastal markets still have deeper specialist pools. The right answer depends on the specific role requirements.
Employer payroll taxes, health insurance, 401(k) matching, and workers’ compensation typically add 20% to 25% to the base salary in total employer cost. For a $150,000 data scientist, budget approximately $180,000 to $190,000 in total employer cost annually. An EOR includes most of these costs within its service structure.
No. An Employer of Record lets you hire US data scientists legally and compliantly without a US entity. The EOR handles payroll, state registration, benefits, and compliance. This is the right model for a first US data hire before you have confirmed your full US team strategy.
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