Top 10 Data Science Companies in the USA 2026

Data science used to mean back-office toil. Some statisticians, a server room, a quarterly report that no one read.

Those days are gone. In 2026, data platforms anchor how American businesses plan, sell and now launch AI agents. The money proves it. Databricks raised US$5 billion at a US$190 billion valuation in August 2026, and Palantir reported 93% year-over-year revenue growth in the second quarter of 2026. Investors, boards and CIOs are all betting that whoever controls clean, governed data will win the AI race.

So who comes out on top? We rank the leaders by revenue size, based on each company’s latest publicly disclosed number as of October 27, 2026: the most recent reported quarter multiplied by four for public companies or, for private companies, the annual revenue, annual recurring revenue (ARR) or run-rate they have announced themselves. These aren’t perfectly comparable metrics, and we note which one we used for each entry. Diversified giants such as Microsoft, Alphabet, Amazon and IBM power much of today’s market too, but they don’t break out data science revenue, so they’re not included on this list.

Also Read: Top 10 Data Science Companies in the UK 2026

What you’ll find here is a blend of platform heavyweights, analytics pioneers and specialist AI software makers. This guide looks at the top 10 data science companies in the USA for 2026, what each one does best and which kind of buyer it suits.

Why Businesses Are Investing in Data Science in 2026

Spending is shifting from experiments to production systems that run day after day. Here’s why American companies keep raising their budgets with data science companies in the USA.

AI Agents Need Governed Data

AI agents are only as capable as the data behind them. That’s why vendors like Databricks, Snowflake and Teradata spent 2025 and 2026 launching agent tools that run directly on governed enterprise data. Without that data foundation, companies often struggle to get past the pilot stage.

Faster, Better Decisions

Forecasting demand, assessing risk and detecting fraud all rely on models that update as conditions change. A strong data science stack means less time between asking a question and getting an answer you can trust. That matters in every industry, from banking to manufacturing.

Regulation and Trust

Lenders, insurers and healthcare providers work under strict rules on how they use data and models, and regulators increasingly expect them to explain their decisions. Tools for lineage, model monitoring and audit trails help them prove those decisions are fair and explainable.

Cost Control

Cloud bills can spiral. Modern data platforms let teams pay only for the compute they use, shift workloads between environments and retire legacy systems, which is why consolidating onto fewer data platforms is a common 2026 goal.

Top 10 Data Science Companies in the USA at a Glance

Top 10 Data Science Companies in the USA (2026)

Here is the full ranking of the leading data science companies in the USA, with the facts behind each place.

1. Palantir Technologies

Palantir Technologies was founded in 2003 by a group that included Peter Thiel and Alex Karp, who remains CEO. In February 2026, the company announced it had moved its headquarters from Denver to Miami. Palantir’s software spans four platforms: Gotham, used by government agencies, Foundry for commercial data operations, Apollo for deployment, and AIP, its Artificial Intelligence Platform.

Palantir is the fastest-growing big name on this list. Revenue for the second quarter of 2026 hit US$1.935 billion, up 93% year over year, and U.S. commercial revenue surged 149%. Palantir closed 220 deals worth US$1 million or more in the quarter and raised its full-year 2026 revenue guidance to US$8.150-8.158 billion. It joined the S&P 500 in September 2024.

Key Data Science Solutions
✔ AIP for deploying AI and agents on enterprise data
✔ Foundry data integration and operations platform
✔ Gotham for defense and intelligence analysis
✔ Apollo for continuous software delivery

Best for: Large enterprises and government agencies that want AI tied directly to operational decisions.

2. Databricks

Databricks was founded in 2013 by the original creators of Apache Spark. Its origins trace back to UC Berkeley’s AMPLab, and it’s headquartered in San Francisco, with co-founder Ali Ghodsi as CEO. Its Data Intelligence Platform is built on the lakehouse architecture and backed by open-source projects its team created, including Delta Lake, MLflow and Unity Catalog.

Databricks says over 20,000 organizations use its platform, including more than 70% of the Fortune 500. In August 2026 it closed a US$5 billion round led by Coatue at a US$190 billion valuation, reporting a revenue run-rate above US$7 billion and year-over-year growth above 80% in the second quarter. More than 1,000 customers spend at a run-rate of over US$1 million, and its data warehousing business alone has passed a US$1.5 billion run-rate.

Key Data Science Solutions
✔ Data Intelligence Platform and Unity Catalog governance
✔ Agent Bricks for building enterprise AI agents
✔ Lakebase, a managed serverless Postgres database
✔ Databricks SQL and Genie natural-language analytics

Best for: Data and AI teams that want one open platform for engineering, analytics and machine learning.

3. Snowflake

Snowflake was founded in 2012 in San Mateo, California, by Benoît Dageville, Thierry Cruanes and Marcin Żukowski. Its principal executive offices are now in Menlo Park, California, and Sridhar Ramaswamy has been CEO since February 2024. As of January 31, 2026, Snowflake had 13,328 customers and 9,060 employees.

Snowflake picked up momentum this year. In its second-quarter fiscal 2027 results, released on September 2, 2026, total revenue rose 35% year over year to US$1.55 billion and product revenue rose 37% to US$1.49 billion. Customers spending more than US$1 million a year grew to 828, with a net revenue retention rate of 126%, and Snowflake lifted its fiscal 2027 product revenue guidance to US$6.07 billion. Recent acquisitions include Crunchy Data and Observe.

Key Data Science Solutions
✔ AI Data Cloud for storage, analytics and sharing
✔ Cortex AI services for LLMs and vector search
✔ Snowflake Intelligence for agent-driven insights
✔ Snowpark for Python, Java and Scala workloads

Best for: Companies that want a fully managed cloud data platform with built-in AI services.

4. SAS

Founded in 1976, SAS is one of the analytics sector’s elder statesmen. It’s headquartered in Cary, North Carolina, and still led by co-founder Jim Goodnight as CEO. The privately held company had 12,170 employees in 2025. Its cloud-native data and AI platform, SAS Viya, was introduced in 2016, and it also sells JMP statistical software.

In its annual report for 2025, its 50th year in business, SAS said it continues to record more than US$3 billion in annual sales. SAS Viya sales grew 20% and cloud sales rose 22%, the sixth straight year of double-digit cloud growth. SAS works with more than 2,700 financial services organizations and more than 1,600 public sector bodies, and it operates in 96 countries. It has been preparing for a possible IPO, with no date set.

Key Data Science Solutions
✔ SAS Viya data and AI platform
✔ Viya Copilot and Viya Workbench for developers
✔ SAS Data Maker
✔ JMP statistical discovery software

Best for: Banks, insurers and public agencies that need proven, well-governed analytics.

5. FICO

FICO, formally Fair Isaac Corporation, was founded in 1956 by engineer Bill Fair and mathematician Earl Isaac. It’s based in Bozeman, Montana, and led by CEO Will Lansing. Its best-known product, the FICO Score, ranges from 300 to 850 in its base form, and Fannie Mae and Freddie Mac have used FICO Scores for mortgage qualification since 1995.

FICO is a data science company at heart: its core business is predictive models that lenders rely on. In its fiscal third quarter of 2026, reported on July 29, 2026, revenue rose 26% year over year to US$674.2 million. Scores revenue reached US$458.9 million, up 41%, software revenue was US$215.3 million, and platform ARR grew 62%. FICO raised its fiscal 2026 revenue guidance to US$2.53 billion.

Key Data Science Solutions
✔ FICO Scores for credit risk decisions
✔ FICO Platform for analytics and decisioning
✔ Industry-specific scores ranging from 250 to 900
✔ Recurring software sold on an ARR basis

Best for: Lenders and financial institutions that need explainable, regulator-friendly risk analytics.

6. EXL

EXL (ExlService Holdings) was founded in April 1999 by Vikram Talwar and Rohit Kapoor and is headquartered in New York City. Kapoor is chairman and CEO. The company says it’s a global data and AI company, with more than 68,000 employees across six continents.

Revenue was US$2,087.7 million in 2025, up 13.6%. In the second quarter of 2026, revenue increased 15.6% to US$594.8 million, and EXL guided for full-year 2026 revenue of US$2.390-2.415 billion. It completed the acquisition of iMerit on August 3, 2026. In 2025 its biggest segment was insurance, followed by healthcare and life sciences, and then banking, capital markets and diversified industries.

Key Data Science Solutions
✔ Data management and analytics services
✔ AI and machine learning model development
✔ Insurance and healthcare analytics
✔ Banking and capital markets analytics

Best for: Insurers, healthcare firms and banks that want an analytics partner with deep industry teams.

7. Teradata

Teradata was founded in Brentwood, California, in 1979 and is now headquartered in San Diego. Steve McMillan has been president and CEO since June 2020. Its Vantage platform and ClearScape Analytics are aimed at large enterprises running analytics across cloud, on-premises and hybrid environments.

Teradata’s business is in transition from on-premises to cloud. Total ARR rose 1% to US$1.509 billion at the end of the second quarter of 2026, while public cloud ARR increased 8% to US$686 million. Second-quarter revenue of US$410 million was flat compared with a year earlier. In 2026 Teradata launched its Autonomous Knowledge Platform, which it says helps enterprises put agentic AI into production.

Key Data Science Solutions
✔ Vantage analytics and data platform
✔ ClearScape Analytics with ModelOps
✔ Autonomous Knowledge Platform for agentic AI
✔ Hybrid cloud and on-premises deployment

Best for: Large enterprises with heavy existing data estates and hybrid needs.

8. Fivetran + dbt Labs

Fivetran was founded in 2012 by George Fraser and Taylor Brown and is based in Oakland, California. On June 1, 2026, it completed an all-stock merger with dbt Labs, the company behind the dbt data transformation framework. Fraser is CEO of the combined company, and dbt Labs co-founder Tristan Handy is president.

When the two announced the deal in October 2025, they said the combined business would have ARR approaching US$600 million. The merged company says more than 100,000 data teams use its tools, with customers including OpenAI, Zendesk, HubSpot, Pfizer, Verizon, Siemens and Roche. Its first jointly built products include dbt Core v2.0, dbt State, dbt Wizard and the open-source Agents Schema.

Key Data Science Solutions
✔ Automated data pipelines and connectors
✔ dbt data transformation and testing
✔ Data infrastructure for AI agents
✔ Integrations with Snowflake, Databricks and Google Cloud

Best for: Data teams that want reliable, low-maintenance pipelines feeding their warehouse or lakehouse.

9. Dataiku

Dataiku was founded in Paris in 2013 and is now headquartered in New York City, with co-founder Florian Douetteau as CEO. Its platform lets data scientists, analysts and business users build, deploy and govern models and AI agents in one environment.

The company said in October 2025 that its ARR had passed US$350 million, with more than 750 customer organizations and over 1,250 employees. In 2026 Gartner named it a Leader in the Magic Quadrant for AI Platforms for Data Science and Machine Learning for the fifth consecutive year. Customers include Roche, Johnson & Johnson, Michelin and Standard Chartered.

Key Data Science Solutions
✔ Collaborative data science and machine learning
✔ Agent Hub for building and governing AI agents
✔ AI governance and model monitoring
✔ Dataiku Online, a managed version for smaller teams

Best for: Enterprises that want data scientists and business teams working on one governed platform.

10. C3 AI

C3 AI was founded in 2009 by Thomas Siebel and is headquartered in Redwood City, California. Siebel returned as CEO in May 2026, with Stephen Ehikian becoming president. The company sells enterprise AI software and applications for industry and government.

C3 AI is in the middle of a turnaround. Revenue for the first quarter of fiscal 2027 was US$52.4 million, with 94% coming from subscriptions, and bookings rose 73% from the prior quarter. For fiscal 2027 it guided for revenue of US$210-240 million. During the quarter it signed 22 agreements with customers such as Ford, Johnson & Johnson, Heidelberg Materials, the U.S. Department of Agriculture and the U.S. Marine Corps, and Forrester named it a leader in enterprise AI.

Key Data Science Solutions
✔ Enterprise AI application platform
✔ AI applications for manufacturers and industrial firms
✔ AI applications for defense and government
✔ Subscription-based AI software

Best for: Industrial companies and federal agencies that want packaged AI applications rather than a build-it-yourself platform.

How to Choose the Right Data Science Partner in the USA

The right choice depends on your data, your team and your rules. These criteria help you narrow down the data science companies in the USA to a shortlist.

Start With the Problem

Decide whether you want a platform, a service partner or an application vendor. Platforms like Databricks and Snowflake give your team tools. Service companies like EXL work alongside you to get the job done. Application companies like C3 AI offer ready-built use cases.

Check Fit With Your Existing Stack

Most large U.S. enterprises already run on AWS, Microsoft Azure or Google Cloud. How does each vendor’s product run on your cloud? How does it handle open formats? And if you ever want to move your data elsewhere, how painful will that be?

Look Hard at Governance

If you work in banking, insurance or healthcare, governance isn’t optional. Ask how well each vendor handles lineage, access controls, model monitoring and audit trails. Vendors with a long record in regulated industries, such as SAS and FICO, tend to be strong here.

Understand the Pricing Model

Consumption pricing starts cheap and can get expensive as you scale. Subscription and seat-based pricing is easier to predict. Ask each vendor for a cost model based on your real workloads, not a demo dataset.

Ask for Proof

Ask for references in your industry, and for a short paid pilot with specific success criteria. Vendors that are confident in their product will agree to both.

Frequently Asked Questions About Data Science Companies in the USA

Which is the biggest data science company in the USA?

On our annualized revenue measure, Palantir and Databricks are the largest data science companies in the USA for 2026, each with annualized revenue above US$7 billion. Microsoft, Alphabet, Amazon and IBM are larger overall, but they don’t report data science revenue separately.

Is a platform or a consulting firm better for a mid-sized business?

There is no single best answer for everyone; it depends on whether you have an internal data team. Platforms like Snowflake, Databricks and Dataiku give you more control if you have data scientists on staff. A services partner such as EXL can often deliver results faster if you don’t have a team yet.

Are data science companies in the USA only for large enterprises?

Not necessarily. Fivetran and dbt Labs say more than 100,000 data teams use their tools, and cloud platforms let smaller teams start with modest workloads and scale as they need.

Why are Google, Microsoft and Amazon not on this list?

They’re major players through Google Cloud, Microsoft Azure and AWS. We left them out because they don’t report data science revenue separately, so we can’t fairly rank them on our chosen basis.

Conclusion

The data science companies in the USA that lead in 2026 are the ones that have turned data platforms into AI platforms. Palantir, Databricks and Snowflake are growing fast on the back of enterprise AI demand, while SAS, FICO and Teradata bring decades of experience in regulated industries.

Specialists such as EXL, Fivetran + dbt Labs, Dataiku and C3 AI fill important gaps, from services and pipelines to collaborative modeling and packaged AI apps. Start with the business problem, check governance and costs, and pick the partner whose strengths match your data and your team.

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