12 Booming Data Science Startups to Watch in the USA in 2025

Data science has emerged as one of the most impactful and in-demand fields in recent years. By leveraging advanced analytics, machine learning, and AI techniques, data science empowers organizations to extract valuable insights from vast amounts of data and make smarter, data-driven decisions.

The United States has become a global hub for data science innovation, with a thriving ecosystem of startups pushing the boundaries of what‘s possible. From healthcare and finance to marketing and cybersecurity, these startups are harnessing the power of data science to solve complex problems and drive transformative change.

As we look ahead to 2024, the data science startup landscape continues to evolve at a rapid pace. In this article, we‘ll highlight 12 of the most exciting and promising data science startups to keep on your radar in the USA. These companies are at the forefront of leveraging cutting-edge data science techniques to drive innovation across industries.

Healthcare & Biotech

1. Tempus Labs

Founded in 2015, Tempus Labs is on a mission to personalize cancer care through the power of data science. By analyzing vast amounts of molecular and clinical data, Tempus empowers physicians to make data-driven treatment decisions and researchers to develop more targeted therapies.

The company has built a massive library of over 5 million clinical and molecular data points, which it uses to train sophisticated ML models. Tempus‘ platform integrates genomic sequencing, AI-powered image recognition, and natural language processing to generate insights that advance precision medicine.

Data scientists at Tempus work on diverse challenges, from developing predictive models for treatment response to identifying novel drug targets. With over $1.1 billion in funding and partnerships with leading academic medical centers and pharma companies, Tempus is poised to transform cancer care through data science innovation.

2. Insitro

Insitro is a biotech startup that‘s leveraging ML and high-throughput biology to transform drug discovery. The company integrates cutting-edge techniques like computer vision, deep learning, and automation to generate and analyze massive biological datasets.

By building predictive models of disease and therapeutic response, Insitro aims to dramatically accelerate the development of new medicines. The company has raised over $700 million in funding and inked major partnerships with pharma giants like Gilead Sciences.

Data scientists at Insitro work at the intersection of ML, biology, and chemistry. They develop advanced algorithms to extract insights from high-dimensional biological data like microscopy images, genomic screens, and proteomics. Insitro is pioneering a data-centric approach to drug discovery that could yield transformative treatments for patients.

Fintech

3. Plaid

Plaid is a fintech platform that enables applications to connect with users‘ financial accounts, powering many of the most innovative financial services and apps. Founded in 2013, Plaid has emerged as the standard infrastructure for fintech, serving over 5,500+ apps and connecting hundreds of millions of user accounts.

Data science and machine learning are critical to Plaid‘s platform, enabling advanced capabilities like transaction categorization, income verification, and fraud detection. Data scientists at Plaid work on diverse challenges across data engineering, data modeling, and ML systems – building the core infrastructure that powers the company‘s services.

In the words of John Pitts, Policy Lead at Plaid: "Data science allows us to better understand customer needs, accelerate app development, and fight financial fraud. It‘s at the heart of our mission to accelerate financial innovation and inclusion."

4. Two Sigma

Two Sigma is a fintech powerhouse and quantitative hedge fund that leverages advanced data science techniques to identify and capitalize on market inefficiencies. The company has built a leadership position in systematic, data-driven investing – managing over $58 billion in assets.

Data scientists at Two Sigma work in cross-functional teams to develop and enhance the firm‘s quantitative trading strategies. This involves mining vast datasets, building predictive models, and optimizing trading algorithms. Two Sigma pioneers cutting-edge approaches to financial forecasting, incorporating techniques like deep learning, NLP, and alternative data.

With a world-class engineering organization and research culture, Two Sigma is consistently pushing the boundaries of data science in finance. Beyond its core business, the firm also incubates and launches new ventures like Venn and Compass that bring sophisticated data science to bear on other industry challenges.

Marketing & Advertising

5. LiveRamp

LiveRamp is a data connectivity platform that enables companies to better leverage their first-party data for targeting, measurement, and personalization. The company‘s platform connects and normalizes data across disparate sources, helping marketers build more accurate profiles of their customers.

Data science is core to LiveRamp‘s offerings, powering identity resolution, lookalike modeling, and attribution capabilities. Data scientists at LiveRamp work on high-scale engineering challenges, like building real-time data pipelines and developing ML models to power the company‘s products.

By making data more accessible and actionable, LiveRamp helps companies deliver more relevant, timely, and measurable marketing experiences. With over 1,000 employees and a market cap of $4 billion, LiveRamp is leading the way in data-driven marketing innovation.

6. BrightFunnel (6sense)

BrightFunnel, now part of 6sense, is an AI-powered account engagement platform that helps B2B organizations predict buyer behavior and engage target accounts at scale. The company‘s platform stitches together intent signals and behavioral data to identify accounts that are in-market and ready to buy.

Data science is at the heart of BrightFunnel‘s predictive modeling and scoring capabilities. By analyzing thousands of data points, the platform can accurately forecast which accounts are most likely to convert, and when. This empowers sales and marketing teams to prioritize efforts and personalize outreach.

Data scientists at BrightFunnel tackle diverse problems in ML, NLP, and time-series forecasting. They‘re building the next generation of intent-based marketing solutions that will make B2B interactions smarter and more efficient.

Cybersecurity & Risk

7. Cybereason

Cybereason is a cybersecurity company that uses behavioral analytics and machine learning to detect and prevent advanced cyber attacks. The company‘s platform continuously monitors user and machine activity to identify subtle signs of malicious behavior.

Cybereason‘s "Malop" (malicious operation) approach leverages data science to stitch together isolated events into a coherent picture of an attack as it unfolds. By detecting the entire attack story, Cybereason empowers security teams to understand and respond to threats more quickly and effectively.

Data scientists at Cybereason come from diverse backgrounds like threat intelligence, malware analysis, and behavioral analytics. They develop novel ML techniques to parse and extract insights from petabyte-scale security data in real-time.

In the words of Cybereason CEO Lior Div: "Data science is fundamental to our mission of reversing the adversary advantage and ending cyberattacks. Our ability to analyze and correlate subtle behaviors across millions of endpoints in real-time gives defenders an edge."

8. BeloFox

BeloFox is a cyber risk modeling platform that enables companies to quantify and manage their exposure to digital threats. The company provides a real-time view of cyber risk, expressed in financial terms, across a company‘s complete IT ecosystem.

To power its platform, BeloFox ingests and analyzes massive amounts of security telemetry from companies‘ networks, devices, and apps. Advanced ML models then translate this raw data into a dynamic view of breach likelihood and financial exposure.

Data scientists at BeloFox operate at the cutting-edge of cyber risk quantification, an emerging discipline that blends techniques from data science, threat intelligence, financial modeling, and software engineering. The company‘s ultimate mission is to help business leaders make smarter decisions around cybersecurity investments and risk management.

AI/ML Platforms

9. Domino Data Lab

Domino Data Lab provides an enterprise MLOps platform that accelerates the development and deployment of data science models. The company‘s platform enables data scientists to build, train, and serve models faster, while providing IT with seamless governance and reproducibility.

With Domino, data science teams can use their preferred tools and infrastructure, but with added capabilities around collaboration, experiment tracking, and automated deployment. The platform is designed to support the full lifecycle, from early exploration to production-scale deployment.

Domino is trusted by over 20% of the Fortune 100, including industry leaders like Allstate, Dell, and Bristol-Myers Squibb. The company has pioneered a model-driven approach to data science that promises to make model delivery and maintenance dramatically more efficient.

10. Weights & Biases

Weights & Biases (W&B) is a machine learning platform that helps data scientists track, visualize, and optimize model performance. With W&B, data scientists can automatically log experiments, compare results across runs, and collaborate more effectively.

The W&B platform is designed to be highly flexible and scalable, supporting any ML library, framework, or cloud. Data science teams at OpenAI, Toyota Research, and hundreds of other organizations rely on W&B to streamline model development.

In addition to experiment tracking, W&B also provides powerful tools for dataset versioning, model evaluation, and pipeline automation. The company is at the forefront of a new generation of tools that aim to make AI/ML model development more reproducible, collaborative, and efficient.

Emerging Opportunities

11. Abnormal Security

Abnormal Security is a cybersecurity startup that leverages AI to protect enterprises from the #1 cyber attack vector: email. The company‘s platform detects and remediates advanced email attacks like phishing, malware, and account takeover by understanding normal user behavior and identifying anomalies.

To stop attacks that evade traditional defenses, Abnormal builds rich behavioral profiles based on organizations‘ unique people, relationships, and business processes. Incoming emails are then analyzed in context, enabling the platform to detect even the most subtle deviations.

Abnormal‘s data science team pushes the boundaries of NLP, graph ML, and deep learning to model email behavior and content at an unprecedented scale. With over $284 million in funding and 10%+ of the Fortune 1000 as customers, Abnormal is defining the future of email security.

12. Viz.ai

Viz.ai is a healthcare startup that applies computer vision and deep learning to detect and triage life-threatening conditions like strokes. The company‘s platform analyzes CT scans and automatically identifies early signs of large vessel occlusion (LVO) strokes, then alerts the care team.

By expediting stroke diagnosis and care coordination, Viz.ai enables patients to receive treatment sooner, leading to improved outcomes. The company‘s technology is FDA-cleared and used by over 1,000 hospitals worldwide.

Data scientists and machine learning engineers at Viz.ai work at the leading edge of medical imaging AI, developing novel deep learning architectures optimized for 3D volumetric scans. The company is also expanding to address other time-sensitive conditions like pulmonary embolism.

With a strong mission orientation and backing from leading investors, Viz.ai is showing the immense potential for AI to transform clinical care delivery.

The Road Ahead

As these 12 startups demonstrate, data science is driving transformative innovation across industries – from healthcare and finance to cybersecurity and marketing. By harnessing the power of advanced analytics, machine learning, and AI, these companies are solving some of the most complex challenges facing organizations today.

Several key trends stand out among these leading data science startups:

1. Verticalized platforms: Many of these startups provide vertical-specific data science solutions (e.g. Tempus in oncology, Abnormal in email security) vs generic platforms. By tailoring their approaches to the unique data and workflows of particular industries, these companies can deliver outsized value.

2. High-scale data & ML: The scale, speed, and sophistication of the data engineering and ML infrastructures at these startups is unprecedented. From real-time feature stores to automated ML pipelines, these companies are pioneering tools and architectures for the next era of data science.

3. Augmented intelligence: While some startups focus on automating away human labor, the most impactful ones augment and empower domain experts to make smarter decisions. By designing data science systems that enhance rather than replace human judgment, these companies drive adoption and impact.

4. Multidisciplinary teams: Tackling complex, vertical-specific data science challenges increasingly requires cross-functional collaboration across data science, software engineering, and domain experts. The most successful startups bring together individuals from diverse backgrounds in the service of a common mission.

As data continues to proliferate and AI/ML techniques advance, the potential for data science-driven innovation will only accelerate. The startups profiled here offer a glimpse of that future – one where data science is woven into the fabric of how every organization operates. For data science practitioners and enthusiasts, there‘s never been a more exciting time to build a career at the vanguard of this transformation.