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Best Data Science Companies Hiring in 2023
Data Science

22 Best Data Science Companies Hiring in 2024

8 minute read | June 27, 2023
Sakshi Gupta

Written by:
Sakshi Gupta

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Big data is transforming industries across the globe. As data increasingly help companies gain valuable insights and make more informed decisions, it’s no surprise that data science jobs are also growing exponentially. 

The U.S. Bureau of Labor Statistics estimates that job opportunities for computer and information research scientists—including data science roles—will grow 22% by 2030, which is much faster than the national average. And Glassdoor recently listed data science roles in its top three jobs this year. 

But with so many companies now hiring for data science roles, how do you know which company is the right fit for you? Here’s our list of the 22 best data science companies hiring right now. 

What Factors Make a Data Science Company Worth Working For?

Before joining the data science job market, you should know which companies will be the best fit for you. When looking for the best fit, ask yourself these questions:

  • Am I interested in building the company’s projects and/or products?
  • Does the company offer competitive compensation?
  • Does the company provide comprehensive benefits like health/life insurance, paid time off (PTO), profit sharing, and retirement benefits?
  • What’s the company’s current market reputation/public perception?
  • Does the company foster a culture of trust, flexibility, collaboration, and positive feedback?

Now that you know what to look for in a data science employer, let’s dive into the top 22 companies hiring today:

Best Data Science Companies: The Big Names

1. Microsoft

Everyone knows Microsoft as a consumer software giant. But it’s also a great data science company to work for, as Microsoft offers a range of products for consumers, developers, and organizations. AI for Earth is a Microsoft initiative developing open-source tools, data, infrastructure, models, and application programming interfaces (APIs) to accelerate the use of artificial intelligence (AI) for environmental sustainability. They also have their AI for Accessibility project, which uses AI technology to empower people with disabilities. 

2. Amazon

Amazon Web Services (AWS), Amazon’s subsidiary cloud computing company, is currently the largest individual market shareholder in the cloud infrastructure service industry. It consistently relies on data to create the best customer experience across all Amazon platforms. Their Automated Reasoning project focuses on automating formal logical reasoning to improve the security, quality, and availability of Amazon’s products and services. And their Computer Vision project uses 3D modeling and imaging to help devices see and understand the visual world.  

3. EY

EY is a global accounting and professional services firm specializing in strategy, consulting, transactions, private company solutions, and corporate finance. EY uses data and augmented intelligence to enhance risk controls, re-engineer processes, and give its clients a competitive advantage. Analytics and AI are embedded into their business processes to generate new revenue opportunities, manage performance, and drive capital allocation strategies. EY utilizes data science for:

  • Business transformation and growth
  • AI consulting services
  • Advanced analytics
  • AI merger and acquisition tools

4. Google (Alphabet)

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Google’s biggest asset is its data. It handles data from all websites using AdSense (Google’s advertising service) or Google Analytics. Then, Google utilizes this data to analyze customer behavior, and refine its host of products and services. Google offers excellent benefits and salaries that are much higher than the industry average.  

5. VMware

A cloud computing and virtualization company, VMware builds technology solutions for multi-cloud businesses. VMware relies on data science to drive research, including anomaly detection, cryptographic agility, cache-adaptive algorithms, and CloudCast. It also has a wide range of active projects like Project Pathway, Remote Memory, and Hillview. Working for VMware as a data scientist, machine learning engineer, or data analyst allows you to join a global community that’s solving critical technology challenges.

6. Walmart

Walmart has tons of existing data to analyze and is also committed to finding new and innovative ways to apply different kinds of data. Walmart engages in comprehensive data mining to discover point-of-sales data patterns, which are then used to make product recommendations, streamline their supply chain, optimize product assortment, and improve store checkout. 

7. JPMorgan Chase & Co.

JPMorgan Chase & Co. uses data analytics to identify patterns in its customers, and in the financial markets. Its vast customer dataset is analyzed to identify risks, find market opportunities, and increase revenue through customer operations. Fortune Magazine has consistently ranked JPMorgan as one of “America’s Ideal Employers” and as one of the “World’s Most Admired Companies.” 

8. PwC

PwC—an international network of professional services firms—utilizes data to assess risk premiums, evaluate the size of a given market, and improve its Total Impact Measurement and Management (TIMM) framework. It also offers comprehensive, flexible, and competitive benefits and salaries.  

Best Data Science Companies: Up and Coming

data science companies

9. Splunk

Splunk is the world’s first data-to-everything central platform, which allows users to search, monitor, and analyze their data more efficiently. Users interact with a web-style interface and use advanced built-ins like outlier detection, predictive analytics, event clustering, and forecasting. These solutions help businesses enhance their digital customer experience, resilience, and cloud transformation. Splunk SOAR (Security Orchestration, Automation and Response), Cloud Platform, Infrastructure Monitoring, and Observability Cloud are all state-of-the-art, rapidly growing projects. Last year, Splunk was featured in Fortune‘s “Best Workplaces in the Bay Area” and CRN‘s “Coolest Cloud Software Companies.” 

10. Numerator

Numerator focuses on customer insights for eCommerce. Many Fortune 100 companies are Numerator clients, and its data scientists work with the client and consumer data to analyze purchase environments, sentiments, behaviors, and outcomes.  

11. Databricks

Databricks is a relatively new company with origins in academia and the open-source community. Its primary product is the Lakehouse Platform, an open and unified platform for data and AI that combines the best of data warehouses and data lakes. Many of the world’s largest organizations—including Shell, HSBC, T-Mobile, and H&M Group—rely on Databricks to enable massive-scale data engineering, business analytics, full-lifecycle machine learning, and collaborative data science.  

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12. Cloudera

Cloudera is a hybrid data cloud company that makes data and analytics easy and accessible for everyone. Clients can combine private and public clouds to unearth more powerful business insights. Cloudera operates within a broad spectrum of industries, including financial services, retail, technology, healthcare, education, and manufacturing.  

13. Teradata

Teradata is another multi-cloud data company providing database and analytics software, products, and services. The Teradata Vantage platform gives clients the flexibility and capabilities to handle massive and mixed data workloads. Teradata also offers cloud analytics, consumption pricing, and cloud data sharing services. You could build a data science career at Teradata in engineering, sales, marketing, consulting, or product management. 

14. Unified

Unified is a leading provider of data-driven social advertising services and solutions. It leverages its proprietary technology to analyze marketing data sets and optimize investments across the consumer journey. Many Fortune 500 companies rely on Unified for these services, and Unified is also a four-time winner of both Ad Age’s “Best Places to Work” and Crain’s “New York City Best Places to Work.”  

15. Alteryx

Alteryx is a leading IT service and consulting firm utilizing data analytics to help organizations solve business and societal problems. Alteryx offers an end-to-end platform that unifies analytics, data science, and process automation to deliver high-impact business outcomes. Its cutting-edge analytic process automation (APA) services turn data into actionable insights, empowering businesses to discover robust solutions. The Alteryx Analytics Cloud, APA Platform, Intelligence Suite, and Designer Cloud are all designed for optimal efficiency. 

16. Civis Analytics

Civis Analytics is a data science software and consultancy company that helps organizations use their data to gain a competitive advantage, fuel innovation, and boost customer engagement. Some of the country’s largest organizations—including Verizon, Discovery, Airbnb, and Boeing—are all Civis clients. Civis relies on an interdisciplinary team of data scientists, developers, and survey science experts to create tailored client solutions. This year, it was voted as one of Built In’s “Best Places to Work in Chicago.” 

17. Sumo Logic

An innovative data analytics company, Sumo Logic focuses on delivering real-time analytics to empower digital businesses. Its scalable, cloud-native Continuous Intelligence Platform enables smarter data-driven decisions and quicker investigations of security and operational issues. Sumo Logic works with Grammarly, Adobe, Airbnb, and The Pokemon Company to provide analytics, including log aggregation and security threats and vulnerabilities. Sumo Logic was featured in Forbes’ list of “Best Cloud Companies” and Fortune‘s list of “Best Workplaces in the Bay Area in 2020.”  

18. Sisense

Sisense is a business intelligence software company that delivers a seamless, simplified, and customizable AI-driven platform. The Sisense Fusion platform provides state-of-the-art data modeling, visualization, embedding, and connectivity capabilities for a range of departments and industries. Sisense was also listed as a “visionary” on the Gartner Magic Quadrant for analytics and business intelligence platforms in February of last year. Its clients include Skullcandy, ZipRecruiter, GitLab, GameDay, Interfolio, and Glytec.

Best Data Science Companies: Startups

19. Algorithmia

Algorithmia is a machine learning model deployment and management solution provider. Recently acquired by DataRobot, it utilizes world-class enterprise-grade infrastructure to manage the production machine learning lifecycle within existing operational processes. Thousands of non-governmental organizations, government intelligence agencies, and Fortune 500 companies leverage Algorithmia to operationalize their machine learning use cases. 

20. Resurface Labs

Resurface Labs specializes in continuous API security. Its primary product is its API management software, Resurface, which enables businesses to capture, store, and explore different API architectures. Resurface also allows clients to use actual user data for faster threat detection, improved troubleshooting, and smarter tests. Because it’s not software as a service (SaaS) company, it gathers real-time actionable data while maintaining private, first-party ownership. Working with Resurface offers a tremendous growth opportunity at a remote-first company. 

21. Cropin

Cropin is an Earth observation and AI-led AgTech organization enabling data-driven farming. It aims to “re-imagine agriculture with data” using a multi-disciplinary approach toward AI, earth observation, agriculture, meteorology, and computer sciences. Cropin leverages pixel-level data from satellite imagery to bring meaningful insight to improve the ag-ecosystem. Cropin currently provides SaaS solutions to 225 agribusinesses, and numerous governmental and non-governmental organizations in more than 50 countries.  

22. TheMathCompany

A hybrid business consulting and services firm, TheMathCompany builds custom AI enterprise applications. It leverages Co.dx, its proprietary AI master engine, to assist with data extraction, algorithm development, performance monitoring, and feature engineering. It has also been named among the Asia-Pacific’s fastest-growing companies by the Nikkei-FT-Statista APAC. 

FAQs About Applying To Work at a Data Science Company

data science companies, FAQs About Applying To Work at a Data Science Company

What Red Flags Should You Look for When Considering a Data Science Company?

A vague job description is one of the first red flags you might spot. You want a clear picture of the projects you’ll be working on and the expertise you’ll need for the job. 

You should also be wary of opportunities that set unrealistic expectations with job responsibilities. Make sure the employer values a healthy work-life balance and will support your career progression. 

Are Data Scientists Paid Well?

Yes. Last year, the U.S. Bureau of Labor Statistics estimated that the mean annual salary for a data scientist was $108,660. This number should increase as the demand for skilled data scientists continues growing in the coming years. 

How Do I Get a Job as a Data Scientist?

Many different career paths can lead to a job as a data scientist. Check out our other blog posts to read more about learning data science from scratch, how to build an awesome data science resume, or the most common data science interview questions and answers

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.