Key Takeaways
- Data science is quite a broad field encompassing various disciplines, techniques, and applications.
- When choosing a data science career, explore your options through internships, online courses, or networking events.
- Data scientists enjoy lucrative careers, with an average salary of $122,738 per year.
There’s a field of study that sits right between that of computer science and statistics—drawing from both and yet standing apart as its own distinct discipline. That field is data science, a relatively new yet immensely influential area of study.
In an age dominated by technology and data, data science has become a part of nearly every industry and its decision-making processes. Unsurprisingly, data science careers are in high demand as they present professions that are financially rewarding, intellectually stimulating, innovative, and impactful.
Top In-Demand Data Science Careers
Data science is quite a broad field that encompasses a range of disciplines, techniques, and applications. Because of this diversity, there are numerous in-demand careers in data science that cater to different interests and skill sets.
The roles listed below are all, in one way or another, connected to the field of data science. They represent the type of opportunities you can pursue with a degree in data science. While some may require additional training, certifications, or specialized coursework, all benefit from a strong foundation in data science principles.
1. Data scientist
We have to start with the data science career that carries its very name—that of a data scientist. A data scientist is a professional who collects, processes and analyzes large volumes of raw information so that they can find meaningful insights from patterns and trends in the data.
You see the products of their work everywhere, even if you don’t always realize it. Think back to the personalized recommendations you see on streaming platforms or fraud detection systems that protect your online purchases. All these are products of the work that data scientists do.
Thanks to their work in transforming raw data into actionable insights, data scientists contribute to decisions and advancements in pretty much all areas of life. They can even be found working in healthcare, using data to predict disease outbreaks, and in transportation, optimizing traffic flow or enhancing navigation systems.
Data scientists enjoy lucrative careers, with an average salary of $108,020 per year.
2. Machine learning engineer
Machine learning, as a technique used in data science, enables computers to learn and make decisions without being explicitly programmed. A machine learning engineer is a professional who develops and implements such advanced algorithms and models.
These engineers focus on building systems that can analyze massive datasets, identify patterns, and help make predictions or decisions. They collaborate closely with data scientists to create and refine models, ensuring they can be deployed effectively in real-world scenarios. It is the work of machine learning that powers innovations like voice recognition in virtual assistants and real-time language translation, among many other systems.
Machine learning engineers earn an average salary of $128,769, with the potential of reaching almost $200,000 per year with additional experience and progression in the workplace.
3. Data architect
Similar to how architects design the blueprints for buildings, data architects help create the frameworks for managing and organizing vast amounts of data. So, essentially, these professionals design and maintain the infrastructure that allows data to be stored, accessed, and analyzed—something that is crucial for the work of many roles in data science.
Data architects define how data flows through systems, establish protocols for data management, and ensure the security and integrity of databases. Without them, the systems needed to support advanced data science projects simply wouldn’t exist.
Their importance for the field is mirrored in their financial compensation. Data shows that the average salary for this role is $145,556 per year, with the potential for further growth.
4. Statistician
The role of a statistician earns its place among data science careers because of its deep connection to the principles and methodologies that define the field. Statisticians often work with data modeling, predictive analysis, and probability theory.
Additionally, many of the skills required for statistics—like coding, data visualization, and statistical computation—are highly transferable, making it a career path that can often be pursued with a degree in data science.
Statisticians focus on analyzing data and interpreting those results to answer specific questions. They help create models that can test hypotheses, improve machine learning algorithms, and refine predictive tools.
The average annual salary for statisticians is around $86,921. However, top earners make over $116,000, so there is also plenty of room for growth.
5. Data engineer
Data engineers deal with data infrastructure, focusing on collecting, transforming, and organizing raw data into usable formats. They aim to improve data flow to be more accessible to other professionals in the field.
These engineers design and maintain pipelines that move data across platforms, helping with its accuracy, consistency, and security. Their work also enables the creation of reliable datasets that power machine learning models, dashboards, and business analytics tools, among other things.
Data engineers earn an average salary of $129,716 per year, with the potential of reaching $177,500 after gaining more experience.
6. Data analyst
Data analysts, true to their name, approach data with the intent of analyzing it and extracting insights. That information is what drives informed decision-making. So, essentially, their work involves interpreting raw data so it’s usable for business intelligence—transforming numbers into narratives.
Their work helps businesses improve their operational efficiency, and they are well-compensated for it. The average salary of data analysts is $82,640, with room for growth into six-figure salaries.
7. Business analyst
Business analysts are another group of professionals that, based on their title alone, one wouldn’t think to associate with data science. However, their role revolves around leveraging data in order to improve business processes, strategies, and outcomes. With skills gained through a data science degree—such as data interpretation, modeling, and visualization—many business analysts are equipped to translate datasets into actionable insights that drive organizational success.
Business analysts often work in teams with data scientists and engineers to align data-driven insights with the strategic goals of their organization.
The average salary associated with this role is around $98,662 per year, with the potential to reach almost $150,000.
8. Database administrator
Database administrators are quite important in data science because they manage and maintain the systems where data is stored. It is their work that guarantees databases are secure and accessible for data scientists, analysts, and engineers.
These professionals handle a range of tasks, including designing database structures, implementing access controls, and setting in place data backups and recovery plans.
The average annual salary for database administrators is $102,260, although salaries can grow even more as you progress in this role.
9. Enterprise architect
Enterprise architects are usually the ones in charge of the overall structure of an organization’s IT systems. Their role revolves around creating frameworks that integrate technology, data, and processes across the organization. Their work is crucial because they establish the infrastructure that enables professionals to collect, store, and analyze data.
These architects must understand data flows, security measures, and scalability requirements in order to design systems that support advanced analytics and machine learning projects.
On average, enterprise architects earn a salary of around $158,841 per year, which is impressive in itself. But, there is room for even more growth as some make over $200,000 in this role.
10. Applications architect
Applications architects help design and develop the structure of software applications, working to guarantee that the product meets user needs and organizational requirements. They rely on data science principles to build tools that integrate with data pipelines, analytics platforms, and machine learning models.
Their work enables data scientists and analysts to access and manipulate data effectively. For example, application architects might design dashboards for real-time analytics or software that automates data processing.
The average applications architect makes around $144,144 per year, though the top earners can reach almost $200,000.
11. Machine learning scientist
Unlike machine learning engineers, whose work is centered on implementing and optimizing algorithms for real-world applications, machine learning scientists tend to be more research-oriented.
Machine learning scientists contribute to innovations like natural language processing, image recognition, and recommendation systems. Their efforts are often the ones that pave the way for engineers to operationalize cutting-edge technology.
Professionals in this role earn an average salary of $142,418 per year, with the potential to reach almost $200,000.
12. Business intelligence (BI) developer
Business intelligence represents the technologies, applications, and practices one can use to collect, integrate, analyze, and present business information. BI developers combine that with principles from data science to design and build systems that enable organizations to transform raw data into insights from which they can benefit.
A BI developer might design a dashboard that tracks data related to sales performance, customer engagement, or supply chain efficiency. So, their work directly impacts how businesses strategize and operate.
BI developers, on average, earn $111,882 per year. However, depending on the years of experience, location, and employer, they can have even higher salaries.
13. AI research scientist
AI (Artificial intelligence) has definitely transformed the way we live and work. Nowadays, we have technologies like self-driving cars, virtual assistants, and predictive healthcare. AI research scientists are the ones behind these advancements, using data science to develop systems capable of learning and adapting.
These scientists work with massive datasets and advanced algorithms to help machines perform tasks that were once thought impossible. Their contributions have impacted and brought change to all industries, from entertainment to medicine.
Scientists in this field make an average of $130,117 per year, with reported salaries reaching as high as $174,000, indicating potential for significant growth.
How to Choose the Right Career Path in Data Science
When it comes to choosing the right career path in data science or in any other field, sometimes it really comes down to “when you know, you know.” So, if you find yourself drawn to a specific role—intrigued by the work and can genuinely imagine yourself happy in that position—that’s your sign.
However, if your vision isn’t so clear, it might help to take some time and assess your interests and strengths. Are you more technical and enjoy coding? Do you lean toward statistics and analysis? Or do you find business strategy and communication more engaging? Having a general idea of such preferences can help you narrow down your options within data science.
If that doesn’t work either, try exploring opportunities through internships, online courses, or even networking events. Speaking with professionals already in the roles you’re considering can provide insights into what each career entails and what you can expect your future to be.
The Bottom Line
Choosing any career in data science is a smart decision. With the sheer volume of data we generate daily—and no signs of slowing down in our reliance on phones, computers, and connected devices—these roles will likely continue to remain in high demand for the foreseeable future.
Applying data science requires a level of expertise and deep knowledge that only comes from specialized education and training. Our Applied Data Science Master’s degree, available both on-campus and online, is designed to equip you with the knowledge and skills needed for various roles. For those not ready to commit to a full master’s program, our Certificate of Advanced Study in Data Science offers a quicker way to advance in this field and gain valuable, marketable skills. Join us, and soon enough, where others see chaos, you’ll be able to find patterns.
Frequently Asked Questions (FAQs):
How do data science careers differ from traditional IT roles?
They differ in the sense that most IT roles typically center on maintaining and optimizing technology systems, while data science is focused on working with data—analyzing and interpreting it for decision-making.
Is it necessary to have a PhD to pursue a career in data science?
No, most advanced data science roles need a master’s degree or equivalent skills; however, a PhD may be required for research-intensive positions.
What industries offer the best opportunities for data science professionals?
Industries like healthcare, finance, technology, e-commerce, and entertainment offer some of the most lucrative opportunities for data science professionals.