Data Scientist Career Outlook: Thriving Opportunities and Future Trends

Data scientists are among the highest paid, fastest growing members of the technology field. The latest BLS figures state a growth rate of 35% and a median pay of $103.5 thousand per year. The field is so popular that major colleges like Berkeley and Yale have introduced degrees for data science.

In this data scientist career outlook, you'll learn more about what it takes to become a data scientist, the top jobs, and the companies who hire these scientists.

Pro Tip: To learn some basics, check out our article on "what is data science" before you get started here.

Data Scientist Job Description

Unlike "regular" scientists, data scientists run tests using information. They use analytical techniques and technologies to answer questions.

Data scientist jobs change depending on the size, needs, and resources the business provides. Each job requires differing education and skills to excel in the field. Below, you'll learn a bit more about the requirements in this data science career outlook.

Required Education

To understand the fundamentals of data science, a college degree provides a great foundation. We've already mentioned a few specific degrees provided by colleges like Berkeley and Yale.

A master's degree in data science is one such example. This means you'll need to pursue an undergraduate degree (bachelor's) before pursuing the highest level of education available.

But fewer employers are requiring college degrees, as jobs that require those degrees fell to 44% in 2021. So, you could feasibly pursue a certification in data science, replacing your educational pursuit with field experience.

Despite the percentage above, these figures refer to college degrees across all fields. So if you can combine a strong educational background with expertise, you'll stand out and command a higher salary.

If you want to start small, check out our list of data science materials for self study. Next, we'll go into the skills portion of this data scientist occupational outlook.

Required Technical Skills

Here are some skills necessary for all data scientist careers:

• Statistical analysis: Being able to draw conclusions from statistical analysis is a must-have skill for those in this position.
• Programming languages: Python is one of the most widespread languages to learn for analyzing information. Some industries rely on additional languages like Ruby.
• Database management: SQL and other database management tools allow data scientists to manage information.
• Problem solving: Reviewing code and finding errors is a big part of what professionals in this field must do.
• Data visualization: Being able to visualize intelligence for stakeholders and decision makers is another common skill across multiple fields.

Each job and industry in data science requires a knowledge of different aspects. Many jobs also require knowledge of cloud computing, APIs, and other technology stacks not mentioned here. Take some time to review job descriptions in industries you're interested in learning how this job description can vary.

For individuals aiming to immerse themselves in cloud technologies and cultivate a thriving career trajectory in cloud computing, we highly recommend perusing our comprehensive guide on navigating the cloud computing career path.

Required Data Science Techniques

Data scientists must leverage a wide range of techniques to draw conclusions from the background. Here are a few examples of some of the more common ones:

• Regression: Unlike classification, regression techniques seek to find predicted values for figures. Examples include linear, lasso, and multivariate regression.
• Classification: Classification of data puts it into different classes to help review it more effectively. Neural networks, decision trees, and Support Vector Machines (SVMs) are three examples.
• Clustering: Similar to classification, clustering seeks to put details into different groups. Examples include DBSCANs (density-based), hierarchy-based, and Gaussian Mixture Models (GMMs).

Data scientists use techniques to better sort and identify anomalies that might fit patterns. Sorting through information is a huge part of any data scientist's position. This ability to manage large volumes of statistics explains why the data science job demand is so high.

To learn more about some of the technical lingo, check out our article: IT language of data analysts.

Top Data Science Jobs

As we said, data scientists cover a wide range of potential skills, and those skills might change the job title. Below, you'll learn a bit more about different jobs and how they are growing.

Data Scientist

From our introduction, you'll already know that data scientists see an average salary of nearly $105 thousand and job growth rates of 35%. This marks a clear demand for them. Even better, the job has a 4.1 out of 5 satisfaction rate with over 10 thousand job openings as of 2022.

This means that data scientists love their jobs and get paid well for doing it. Therefore, it's considered one of the top jobs you can get in today's workforce.

Data Architect

Data architects are specialized professionals who organize, store, and secure information. Architects see an average salary of $112 thousand, but see a growth score of 8%. Compared to other jobs, this is a much weaker data science future outlook (but still above average)

Data architects work in industries that need large statistical fields, like insurance. Their expertise requires more work in managing databases through programs like SQL.

Data Engineer

While architects do the organizing, data engineers build systems for managing information. Job growth rates for data engineers are at 22%, seeing average salaries of about $109 thousand. This is slightly lower than the overall field in data scientist job growth.

Engineers might spend more time building the system, but can also handle the collection and presentation of statistics. The duties of a job engineer can cross over into other fields, so pay attention to the job description before applying.

Data Analyst

Data analysts focus more on drawing conclusions from the details, but have a lot in common with scientists. They command high salaries as a result, but those might shift depending on the industry. The data scientist job market varies between different job categories.

For example, financial analysts make $96 thousand per year and are seeing growth rates of 8%. This is one industry-specific job a data analyst can take, but there are many other options depending on the expertise of the analyst.

Machine Learning Engineer

Machine learning engineers specialize in working with AI (artificial intelligence) systems. Because of this specialized skill set, salaries are around $133 thousand, with growth rates leading into 2027 of about 40%. This subcategory marks the highest growth in the job outlook for data scientists.

With popular tools like ChatGPT coming to the forefront, companies are racing to compete. As AI continues to be a growing aspect of business software, you can expect this job to stay popular for a long time.

Business Intelligence Engineer

Business intelligence engineers specialize in managing information for large, enterprise-level organizations. A BI engineer commands a salary of about $116 thousand, with growth rates around 8%.

BI engineers aid organizations in pursuing their goals using data-driven decisions. This is a more specialized job, focusing mainly on organization growth.

Where Do Data Scientists Work?

Now that you know about the different careers that data science includes, let's dig into where you can find them working. You can learn about the top companies, industries, and locations for below:

Top Companies

Below, you'll see the top ten companies hiring data scientists based on the latest Glassdoor figures:

1. IBM
2. Microsoft
3. Amazon
4. Meta (Facebook)
5. Booz Allen Hamilton
6. JP Morgan Chase & Co
7. Google
8. Capital One
9. Deloitte
10. Nielson

Those in tech will find some recognizable names in IBM, Google, and Microsoft. For many, these companies are the dream regarding data science job prospects. But you'll also find a healthy number of those in the financial industry, which is represented in our next section.

Top Industries

According to 2022 BLS figures, these industries employ scientists.

The top industry for data scientists includes technical fields, which makes sense. The second two industries include insurance (both financial and informational), as they have to manage large volumes of statistical details to set insurance rates.

As field growth continues, you might see some of these numbers shift. So, keep your eyes open to know more about the data science future job market.

Top Locations

So, where do you see most of these employees? Of course, it depends on the location. Here are the top ten cities where you can find high-paying data scientist careers in the USA (in annual thousands):

1. Los Gatos, CA - $455 (annual)
2. Cupertino, CA - $293
3. Cambridge, MA - $264
4. Menlo Park, CA - $263
5. Sunnyvale, CA - $259
6. Mountain View, CA - $259
7. San Francisco, CA - $242
8. San Jose, CA - $239
9. Los Angeles, CA - $235
10. Palo Alto, CA - $232

It's no surprise that the state housing Silicon Valley, which hosts some of the largest tech companies in the world, dominates the list. Honorable mentions include Boulder, CO ($224 thousand) and Boston, MA ($184 thousand).

Data Scientist Salaries Based on Experience

Salaries can vary depending on industry and specialization. Experience is also a major factor, and here's a table breaking down how salary changes depending on experience:

The information above comes from the latest Glassdoor intelligence.

Future Trends for Data Scientists

Want to prepare to learn more about data science career futures? Here are some trends to keep your eyes out for:

• Generative AI: Earlier, we mentioned the massive growth of machine learning jobs. This is thanks to the current and continuing popularity of generative AI technology, which includes programs like ChatGPT and DALL-E. This trend is rapidly evolving, so keep your eyes open on how it changes the world.
• Cloud Systems: It's becoming increasingly common for technology experts to need to understand cloud systems. Cloud computing is expected to reach $2.5 trillion by 2031, so expect more businesses to join the cloud migration.
• Edge Intelligence: The IoT (Internet of Things) is a growing market, but it's expected to be followed by the AIoT (Artificial Intelligence of Things). Edge Intelligence leverages mobile devices and the IoT as an expansion of the field. The idea is to improve response times by processing information closer to where it's needed, which is a big change from massive warehouses.

These are just three major examples of what to expect in the coming (and current) years. Stay informed about the latest industry trends so you know what you need to learn. The job market for a data scientist could change depending on how these trends emerge.

Conclusion

With this data science job outlook coming to a close, it's clear that more people will be needed. With this change in the data science job market, you can expect it to become more competitive as the need grows, inviting more people to pursue impressive salaries in the jobs they love.

The opportunity: you can stay ahead of the curve by knowing your value. Using the information above, you won't have to guess data science job growth, you already know.