Which Data Science Careers Are Right for Me?

Data science is an ever-growing, exciting career path for people who love math and science. If you’re a student looking to enter an innovative and analytical field, data science might be a good fit.

While all data science careers focus on, well, data, how they work with that data depends on their type of role. Some professionals in data science focus on ensuring data can be collected, stored, and accessed efficiently. Other data scientists focus on analyzing that data, with some using insights to help businesses make decisions and others using it to create algorithms for machines to learn.

If you’re interested in data science careers, how do you determine which one is right for you? In this guide, we’ll go over some of the main types of data science careers, share a fun, quick quiz to help you determine which one might be your best fit, and share expert insights on how to land a data science career.

What Is Data Science?

First, what does “data science” actually mean? And what does a career in the field look like?

At its core, data science is the study of data. This industry — which combines math, statistics, computer science, and even business strategy — is all about using data to solve problems and make decisions. 

Think about the massive amounts of data we generate every day — every Google search we make, every Instagram post we like, and even the steps we take when our phone is in our pocket. Data science looks deeper into that data to turn it into actionable, meaningful insights. Professionals in the field will then use that data to uncover patterns, predict trends, train new technology, and help businesses, governments, and organizations make smarter choices.

Data Science Career Path

The data scientist career path varies depending on what kind of data science career you choose and whether you’re looking to do more project-oriented work or manage a team as you grow. 

Most data science professionals start in entry-level roles where they’re doing more hands-on data work like cleaning data or basic analysis. As you grow in the career path, you start to become more autonomous and take ownership of data projects. When you’re more senior, you’ll likely have more impact on both data and business strategies. 

The job outlook for the data science career path is incredibly strong, with a 36% projected percent change in employment from 2023 to 2033 (compared to an average growth rate of 4% for all occupations). Professionals in data science tend to get paid well, too, with a mean annual wage of $119,040.

No matter what path you choose, most data science roles require you to be curious and comfortable working with data. You’ll need to be ready to learn a variety of skills, from programming and data visualization to advanced mathematics and machine learning (ML). As you grow, you’ll likely focus more on big-picture strategy and business decisions — but the data will still drive your work! 

Types of Data Science Careers

So, what kind of data science career is right for you? Here are some of the main types of data science careers, including what they work on, what skills they use, and what types of people might thrive in these roles. 

Data Analyst

A data analyst uncovers insights from data to help businesses make better decisions. These professionals clean and analyze data sets to find patterns and trends. From sales performance to customer behavior, data analysts use their findings to generate reports, dashboards, and visualizations that guide business strategies.

>>MORE: Data Analyst vs. Data Scientist: What’s the Difference?

Data analysts are detail-oriented problem-solvers. While data analysis may not always be fast-paced like other areas, it offers a balanced work-life environment and strong growth potential. Analysts often progress into senior roles or pivot into specialized fields like business intelligence.

Machine Learning Engineer

Machine learning engineers focus on creating algorithms that allow machines to learn and make decisions without human intervention. Whether it’s developing a recommendation engine for a streaming service or improving fraud detection for a bank, machine learning engineers play a key role in shaping intelligent systems.

If you’re fascinated by artificial intelligence (AI), enjoy coding, and want to work on cutting-edge projects, machine learning engineering could be the perfect path. This field is fast-paced and constantly evolving and offers tremendous career growth, with opportunities to work in industries like tech, finance, healthcare, and more.

Data Engineer

Data engineers are responsible for designing, building, and maintaining the infrastructure that allows data to be efficiently collected, stored, and accessed. They ensure that data pipelines are optimized and that systems can handle large amounts of information. A data engineer’s work often serves as the foundation for data scientists and analysts to perform their tasks.

If you enjoy working behind the scenes, love technical challenges, and are detail-oriented, this career could be a great fit.

Research Scientist

Research scientists in data science work on cutting-edge techniques and algorithms that push the boundaries of what AI and data-driven technologies can achieve. Their work may involve:

• Developing new machine learning algorithms
• Working with big data
• Researching novel applications for AI in fields like healthcare, robotics, or climate science

If you’re passionate about innovation, enjoy diving into complex problems, and thrive in academic or research settings, this could be the ideal role for you. It’s perfect for those who want to be at the forefront of data science and contribute to breakthroughs in the field.

Which Data Science Careers Are Right for Me? Quiz

How to Land Data Science Careers

Now that you’ve learned what kind of data science career is right for you, how do you actually land a role in the field? We asked data science professionals for their best career advice.

Build the Foundation Skills

Regardless of what kind of data science career you choose, technical and soft skills are essential to any role in the field.

“Get a foundation in some programming language (Python, R, SQL are always good starts), and develop a firm foundation in mathematical and statistical concepts,” says Paul Harmon, senior manager of data science at Atrium. 

>>MORE: What Are Programming Skills?

Connie Yang, managing principal of data science and ML at DesignMind, agrees that strong SQL and Python skills are crucial. “These are the bread and butter of data science,” she says. “Being proficient in both is essential for handling data, building models, and working with various tools. Python, especially, is critical for machine learning and statistical analysis.”

Yang also recommends getting experience with cloud platforms like Azure, AWS, or GCP. “It’s not just about knowing how to analyze data but understanding the infrastructure behind it and how data science solutions can be deployed at scale,” she says. “This kind of experience gives you the bigger picture of how data science fits into real-world applications.”

But it’s not only the technical skills that matter to a data scientist — soft skills, or how you communicate and collaborate with others — are crucial when sharing your data insights.

“Because data scientists often have to hone their communication skills and work with non-technical stakeholders, it’s also valuable to develop good technical writing and presentation skills,” Harmon says. 

Work on Data Science Projects

How can you start getting some of the skills you need to land a data science career? 

“Don’t just study — build,” Yang says. “Dive into projects that push boundaries. Whether it’s exploring large and small language models, hacking together something new at a hackathon, or enhancing existing tools on GitHub, make sure you’re constantly applying what you learn to real-world problems. Practical experience is what sets you apart.”

Max Dugan-Knight, a climate data scientist at Deep Sky, recommends starting with an end-to-end data science project.

“Pull raw data from somewhere (Our World in Data, public API, scrape web pages, etc.), process and visualize it in Python or R, and use the data for something useful: answer a research question, build a machine learning model, fine-tune a Gen AI LLM for a specific purpose,” he says. “There are many options, but the bottom line for me is showing you can do something is much more powerful than telling, i.e., listing skills on your resume.”

Having an online portfolio of data science work shows that you have the experience needed to drive impact from day one.

Consider Your Chosen Industry

Careers in data science tend to have two main aspects: specific expertise and a specific industry. 

“Data scientists usually have specific skillsets they are experts in (natural language processing, visual AI, MLOps, Gen AI, data engineering, etc.), and they may also have domain areas they are experts in (finance, insurance, healthcare),” Dugan-Knight explains. “It’s worth thinking about both of those and identifying what you want to be doing day in and day out from a skillset perspective, as well as the domain you are most interested in working in in the long term.”

It doesn’t matter which you pick first (a type of skillset or domain). However, Dugan-Knight calls out that domain expertise is typically overlooked and undervalued by younger data scientists when it can be one of the most crucial parts of your career path in the long term.

“No matter how good your data science skills are, it’s hard to move from one industry area to another,” he says. “That domain knowledge will make you stand out. Data science doesn’t exist in a vacuum, so understanding the landscape in which you’re working is crucial.”

>>MORE: Learn what it’s like to work in data science in various industries, from working in air transportation with British Airways’ Data Science program to working at a bank with Commonwealth Bank’s Introduction to Data Science program.

Be Open (and Adapt) to Change

Data science has a strong job outlook because it’s a growing industry. While this is great for job prospects, it also means that the field is adapting. As new technology emerges, data scientists are jumping in to develop new systems and explore what’s next. 

“The data science space is changing so fast,” Dugan-Knight says. “The options available to me even four years ago when I graduated with my Masters are totally different from what’s available today. GenAI and LLMs didn’t really exist. Stay flexible and always be ready to learn new skills.”

Staying on top of industry news can help you prepare for what’s ahead. Following industry leaders allows you to keep on the pulse of changes and understand what new skills and expertise are becoming more valuable in the field. 

For example, Yang says that given the surge in automation and generative AI, focusing on building AI engineering skills is crucial. “The future of AI and data science is about more than just solving problems — it’s about creating solutions that can scale and have a real-world impact,” she says. “To truly make a difference, it’s not just about technical know-how but understanding how to deploy these systems at scale, ensuring they’re both effective and ethical.”

Data Science Careers: The Bottom Line

Data science is a fast-growing industry buzzing with opportunity. If you’re interested in an analytical career path that applies math and science to find patterns and uncover strategic insights, data science can be a great career choice.

Depending on your specific interests, career goals, personality type, and even preferred work environment, there are various data science careers, from a data analyst who helps businesses grow to a research scientist who explores the next development in AI technology. 

If you’re looking to land a career in data science, the best way to stand out is to get foundational skills and start building projects. You can gain those skills and experience with Forage job simulations — free, self-paced, virtual programs from top employers that prepare you for the working world.

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