Companies using scikit-learn

scikit-learn is a free, open-source machine learning library for Python that provides simple and efficient tools for data analysis and modeling, featuring various classification, regression, clustering algorithms, and utilities for model selection, preprocessing, and evaluation.
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Companies using scikit-learn

Technology
is any of
scikit-learn Technology Logo/Icon
scikit-learn
company
COUNTRY
Tech confidence score
REVENUE
# Tech JOB POSTINGS
Affirm
United States Country Flag Icon
United States
$1.2B
770
Amaris Consulting
Switzerland Country Flag Icon
Switzerland
-
4,604
Blue Yonder
United States Country Flag Icon
United States
$125M
489
Carvana
United States Country Flag Icon
United States
$11.1B
500
FanDuel
United States Country Flag Icon
United States
$3.2B
353
Forvis Mazars
Spain Country Flag Icon
Spain
-
717
Grupo TECDATA Engineering
Spain Country Flag Icon
Spain
-
141
KnowBe4
United States Country Flag Icon
United States
-
90
Lightspeed
United States Country Flag Icon
United States
$63.1M
340
Optum
United States Country Flag Icon
United States
-
716
Showing 10 of
32,955
results
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3,296

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32,955
companies actively hiring with
scikit-learn
technology, including firmographic data,
76,495
developer profiles working on that technology, and direct contacts to engineering leaders within your target accounts.
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scikit-learn Competitor Technologies

No. of companies use the technology

Developers using scikit-learn

NAME
contact
DESIGNATION
COUNTRY
Company
Total tENURE
Scott Mcallister
MLOps Engineer
United States Country Flag Icon
United States
CGI Company Logo
CGI
7 years
Mayank K
Senior Cloud Data Engineer
United Kingdom Country Flag Icon
United Kingdom
Birlasoft Company Logo
Birlasoft
4 years
Mark Santolucito
Technical Staff
United States Country Flag Icon
United States
Sinch Company Logo
Sinch
3 years
NAME
contact
DESIGNATION
COUNTRY
Company
Total tENURE
Elizabeth Siegle
VP Data Engineering
United States Country Flag Icon
United States
Fastly Company Logo
Fastly
4 years
Evan Zamir
Vice President
United States Country Flag Icon
United States
Goodgame Studios Company Logo
Goodgame Studios
8 years
Julio Silva
Senior Cloud Developer
Germany Country Flag Icon
Germany
Distributel Company Logo
Distributel
4 years

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76,495
developers actively working with
scikit-learn
technology, including economic buyers data for each account, complete with verified contact information, role tenure, company context, and adoption signals.
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What would you like to do with company-level technographics data that are using scikit-learn?

Build my TAM account list or assign accounts to my sales team

Transform your desired technology user data into actionable sales territories by combining firmographic ICP criteria with real-time technology adoption signals. Traditional account assignment based solely on company size and industry leaves money on the table—successful DevTool sales teams prioritize accounts showing active technology expansion signals.

Strategic account prioritization framework

Building an effective Total Addressable Market (TAM) requires more than basic firmographic filters. Companies using your desired technology represent varying levels of buying intent depending on their implementation stage, team growth, and technology stack evolution. Learn our complete framework for building DevTool ICP account lists to establish the foundation for strategic account segmentation.

Confluent ICP scoring example illustrating core, broader, and relevant universe tiers based on Kafka adoption and data streaming scale

Standard ICP criteria—geography, industry, company size, revenue—only provide baseline qualification. High-performing sales teams layer technology hiring signals on top of firmographic data to identify accounts actively expanding their technical capabilities. Companies hiring  for your desired technology engineers or architects signal active investment in the technology stack, indicating higher purchase intent and budget availability.

In-market account identification and assignment

Priority account assignment should factor in recent technology hiring patterns as a proxy for market timing. Companies posting jobs for Redis engineers, Kubernetes specialists, or React developers demonstrate active technology expansion—making them significantly more likely to evaluate complementary tools within 90 days.

Our LinkedIn outreach playbook details the specific process for identifying and assigning these in-market accounts to sales teams. This approach increases meeting acceptance rates by 40% compared to generic outbound because prospects are already in active buying mode.

Territory assignment best practices

Tier 1 accounts: Companies using your desired technology with recent hiring activity for related roles. These accounts get immediate sales attention with personalized outreach referencing their specific technology initiatives and hiring needs.

Tier 2 accounts: Established desired technology users without recent hiring signals but strong firmographic fit. Assign these accounts for longer-term nurture campaigns and quarterly check-ins to monitor technology expansion signals.

Tier 3 accounts: Companies using your desired technology with weaker ICP fit or unclear expansion signals. Route these accounts to inside sales or marketing-qualified lead campaigns until stronger buying signals emerge.

Refresh account assignments monthly based on new hiring signals and technology adoption data. Companies can move between tiers quickly as their technology needs evolve, and sales territories should reflect these dynamic market conditions rather than static demographic assignments.

The combination of your desired technology usage data and hiring intelligence creates a predictive framework for sales success, ensuring your team focuses energy on accounts most likely to convert within the current quarter.

Learn more

Run marketing Campaigns

Leverage your desired technology user data to create targeted campaigns across three distinct audience levels: companies, developers (practitioners), and economic buyers. Each audience type requires different messaging, channels, and campaign strategies to maximize conversion rates.

Multi-level audience targeting

Companies: Target organizations using your technology of choice for account-based marketing approaches. Focus on company-level signals, firmographics, and technology stack intelligence to build high-intent prospect lists.

Developers (contacts): Reach practitioners who directly implement and use chosen technology. These technical decision-makers influence tool adoption and can become internal champions for your solution.

Economic Buyers (contacts): Target executives and budget holders at companies using your desired technology. While they may not use the technology directly, they control purchasing decisions and strategic technology investments.

Campaign strategies by audience type

ABM Google/LinkedIn Ads to your TOFU audience: Run account-based display campaigns targeting companies using containerization technologies like Docker or Kubernetes. Create awareness-stage content about DevOps optimization, infrastructure costs, or developer productivity to capture early-stage interest from decision-makers.

Invite developers to topical webinars: Host technical webinars for Redis users about database optimization, caching strategies, or microservices architecture. Developers using Redis are likely interested in performance engineering topics that showcase your platform's capabilities in a educational, non-sales context. Leading DevTools like Galileo and Camunda use this strategy effectively—see how they leverage expert-led sessions to grow their TOFU audience by educating and nurturing developer communities around emerging technologies.

LinkedIn outbound campaigns to developers or economic buyers: Execute targeted LinkedIn outreach to practitioners and buyers at companies using complementary technologies. See how Kubegrade leveraged Kubernetes user data to run successful LinkedIn and email campaigns, or follow our proven LinkedIn outreach playbook that helped Unstructured book meetings with economic buyers.

Competitor email campaigns based on competitor technology: Target companies using competing solutions like MongoDB (if you're in the database space) or Elasticsearch (for search solutions). Craft messaging around migration benefits, performance comparisons, or feature gaps that position your solution as the superior alternative.

Complimentary technology campaigns: Run campaigns to companies using GraphQL (if you provide API tools) or React (for frontend development solutions). Focus messaging on how your product enhances their existing technology investments rather than replacing them—creating additive value propositions.

Technical content nurture campaigns to developers: Send regular technical newsletters to PostgreSQL users featuring database optimization tips, query performance guides, or architectural best practices. This builds relationship equity with practitioners who influence purchasing decisions while demonstrating your platform's technical depth.

Campaign execution framework

Each campaign type works best when aligned with the prospect's technology maturity and buying stage. Companies actively expanding their technology usage often have budget allocated for complementary solutions, making them higher-intent prospects than those just beginning adoption.

Combine multiple campaign types for maximum impact: start with educational content to developers, then retarget engaged prospects with ABM campaigns to economic buyers at the same companies. This multi-touch approach increases conversion rates while building relationships across the entire buying committee.

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How to target companies using scikit-learn

How to build your target account list?

Start by building your Ideal Customer Profile (ICP) universe using technology signals as a foundation. Companies using

scikit-learn

often share similar technical maturity and infrastructure needs, making them prime candidates for developer-focused solutions. Learn our complete framework for building DevTool ICP account lists to maximize your targeting precision.

Customize this data by filtering for geography, industry, company size, revenue, technology usage, job positions and more. Our platform provides technology intelligence at both company and individual levels—categorized into developers/practitioners and economic buyers within those organizations. This dual-layer approach enables precise targeting whether you're running ABM campaigns at the account level or personalized outreach to specific contacts.

Download your refined lists in Excel or CSV format, sync directly to your CRM (HubSpot, Salesforce), or use our APIs to send data to your warehouse. For individual-level targeting, explore our

developers using
scikit-learn

database for direct practitioner and buyer intelligence.

How to get alerted when new companies adopt scikit-learn technology?

Set up automated alerts to capture companies as they adopt

scikit-learn

in real-time.

This gives your sales team first-mover advantage when prospects are actively evaluating and implementing new solutions—the optimal time for outreach.

Configure alerts based on your specific ICP criteria: get notified when companies in your target geography, industry, or size range start using your target technology. Alerts are delivered directly to your inbox with complete company and contact intelligence, enabling immediate, contextual outreach while the technology adoption signal is fresh.

How to sync this data with my CRM or sales stack?

Export technology user data seamlessly into your existing sales and marketing infrastructure. Direct CRM integrations with HubSpot and Salesforce automatically sync company and contact records with technology intelligence, enriching your existing database.

Use our API endpoints to send

scikit-learn

user data directly to your data warehouse, enabling advanced segmentation and analytics across your entire revenue stack. This approach works particularly well for companies running sophisticated ABM programs or complex lead scoring models.

The targeting strategy differs significantly between contact-level outreach and account-based campaigns. For individual targeting, focus on practitioners who directly use

scikit-learn

with personalized technical messaging. For ABM approaches, target economic buyers at companies using

scikit-learn

with broader business value propositions and multi-threading strategies.

Frequently Asked Questions (FAQ)

What is scikit-learn?

scikit-learn is a cutting-edge technology that falls under the category of Machine Learning Libraries. It provides a comprehensive suite of tools for data mining and data analysis, implemented in Python and built upon NumPy, SciPy, and matplotlib. Developed in 2007 as a Google Summer of Code project by David Cournapeau, scikit-learn has evolved into one of the most widely used machine learning libraries in both academia and industry. Its primary goal is to make machine learning accessible to non-specialists while remaining efficient and productive for experts.

The technical architecture of scikit-learn follows a consistent and intuitive API design that makes it exceptionally user-friendly. It implements a wide range of supervised learning algorithms (including Support Vector Machines, Random Forests, Gradient Boosting, and Neural Networks), unsupervised learning techniques (such as k-Means, DBSCAN, and PCA), and tools for model selection and evaluation. What sets scikit-learn apart is its focus on computational efficiency, code quality, and extensive documentation. The library emphasizes reproducibility and transparency in its implementations, making it ideal for production environments where reliability is crucial.

In terms of market adoption, scikit-learn has become the de facto standard for machine learning in Python. It's widely used in data science competitions, research papers, and commercial applications across industries. The project maintains a strong open-source community with hundreds of contributors and is actively developed to incorporate new algorithms and methodologies. As machine learning continues to transform industries, scikit-learn's commitment to accessibility, performance, and reliability ensures its continued relevance in the evolving landscape of data science and artificial intelligence.

What is the source of this data?

We aggregate developer & company technographics intelligence from multiple proprietary and partner sources. Our platform monitors job postings across millions of companies—tracking listings on career sites, job boards, and recruitment platforms to identify technology adoption patterns and internal tool usage. This hiring signal data reveals what technologies organizations are actively investing in.

Beyond job data, Reo.Dev maintains a proprietary database of 30+ million developers and tracks activity across public GitHub repositories to capture real-time technology usage signals.

We supplement this with GDPR-compliant datasets from trusted data broker partners and visitor intelligence platforms, creating a comprehensive view of both company-level tech stacks and individual developer behaviors.

This multi-source approach ensures you're working with the most accurate, up-to-date company technographics & developer intelligence available.

How often is the data updated?

Our platform refreshes data daily, giving you access to the latest developer and technology intelligence. This continuous update cycle ensures your go-to-market teams are working with current information that reflects real-time market movements, emerging technology adoption patterns, and fresh hiring signals from across the industry.

What companies use scikit-learn?

Some of the companies that use scikit-learn include Affirm, Amaris Consulting, Blue Yonder, Carvana, FanDuel, Forvis Mazars, Grupo TECDATA Engineering, KnowBe4, Lightspeed, Optum, and many more. You can find a complete list of 32,955 companies that use scikit-learn on Reo.Dev.

Who uses scikit-learn? Which industries use scikit-learn?

scikit-learn is used by a diverse range of organizations across various industries, including "Healthcare and Life Sciences", "Financial Services", "E-commerce", "Technology and Software", "Research and Academia", "Manufacturing". For a comprehensive list of all industries utilizing scikit-learn, please visit Reo.Dev.

How many customers does

scikit-learn
have?
As of now, we have data on
32,955
companies that use
scikit-learn
.

Where is scikit-learn adoption highest worldwide? In which countries scikit-learn is used the most?

According to usage insights, scikit-learn sees the strongest adoption across several major tech hubs. United States leads with 9,568 companies using it, followed by India (2,194) and United Kingdom (2,049).Other regions with significant scikit-learn usage include France (1,071), Germany (915), Canada (770), and Netherlands (386).Overall, scikit-learn enjoys widespread implementation globally, powering applications across diverse industries and regions.

How to find companies that use

scikit-learn
?

Visit reo.dev and use Reo.Dev's audience builder to search for companies using your desired technology—our platform analyzes job postings, GitHub repositories, and proprietary developer data to identify the  technology stack for any given organization. Book a demo with us today to get started.

How to get an updated list of companies that use

scikit-learn
?

Reo.Dev provides real-time access to companies using your desired technology of choice and thousands of other developer technologies. Our platform continuously tracks technology adoption signals from job postings, GitHub activity, and proprietary developer data to give you the most current view of which organizations are actively using the technologies in their tech stack. Simply search for your desired technology within our audience builder to generate a targeted list of companies—complete with firmographic data, hiring signals, and tech stack intelligence. Book a demo with us today to get access to the latest data.