Ever wondered how Instagram always shows you the perfect ad? Or how your local supermarket keeps the shelves stocked just right? Chances are, a data scientist is behind it.
You’ve probably heard the term “data science” a lot lately. It’s a fast-growing, high-paying field—and nearly every industry and organization is looking for people with these skills.
But what exactly is data science, and what does the job involve? As a data scientist myself and the founder of for the love of grad—where I help clients get into top PhD and MS programs with funding—I can break it down for you. I’ll explain what data scientists do, what the job is like, how to become one, and why you might want to consider it.
What Is a Data Scientist, and What Do They Do?
Data scientists write code to model and solve business problems. Take those Instagram ads, for example. Using all the data collected, a data scientist builds a model or algorithm—one flexible enough to apply to every user but precise enough to recommend the right ad for each person at the right time. And that’s no easy task.
The amount of data generated every day is massive—and it’s only getting bigger. Tools like Excel can’t handle it all. Instead, data scientists use programming languages (mostly Python) to make sense of it.
You might think, Okay, so you just dump the data into some magic machine, and it spits out the answers, like “What ad should this user see right now?” Not quite. A data scientist has to build that “magic machine”—a model tailored to their company’s goals.
It all starts with asking the right questions. “The most important skill for a good data scientist is critical thinking,” says Kate Lyons, a senior data scientist at PPG Industries. Just plugging data into a pre-made algorithm won’t make a real impact. You have to break down the problem and ask the right questions—otherwise, you’ll solve something nobody cares about.
Once you have a clear question, you explore the data to spot patterns and trends, then experiment with different models. “You need to really understand the methods you’re using to avoid costly mistakes or misleading results,” Lyons adds. Data scientists know which models generally work for certain problems, but fine-tuning the best one is an art—like creating a model that says, “At 8:05 PM, show Vincent a sunglasses ad” (and doing this thousands or millions of times for different users at different times).
After building what you think is the best model, you work with the project team and stakeholders to ensure it delivers real value. This leads to another key part of the job: storytelling. “You should be curious about the data and the stories it can tell,” says Abhilash Nair, a lead data scientist at FICO. You have to present your findings in a way that leadership and colleagues can understand. With your new model, your story, and your willingness to accept imperfection (no model is perfect right away), you convince stakeholders this is the best path forward.
Finally, the product team turns your code into something practical—whether it’s solving an internal business problem (“How much cereal will we sell next week?”) or improving a customer-facing feature (“Which ad belongs in this user’s feed?”).
While responsibilities vary, most data scientists:
- Frame business questions so they can be answered with data
- Investigate data to find what’s useful (and what’s flawed)
- Work with data engineers to ensure the right data is available
- Write code to model data
- Collaborate with teams to explain the model’s business value
- Constantly learn new tools and frameworks
What Job Titles Exist in Data Science?
“Data science” is a broad field with many roles. Here are some common ones:
- Data Scientist: Builds AI/ML models to analyze data and make predictions
- Machine Learning Engineer: Does the same work as a data scientist
- Artificial Intelligence Engineer: Also does the same work
- Big Data Analyst: Similar to a data scientist but may focus more on visualizations than AI
- Pricing Strategy Data Scientist: Specializes in setting product/service prices
- Risk Analyst: Uses stats and coding to assess risks in large datasets
- Data Engineer: Builds pipelines to collect and store data
- Cloud Data Engineer: Sets up cloud-based data infrastructure
A data scientist’s role can vary, but the core work is coding and modeling. They might also work on visualizations, but that’s usually handled by software developers.
What’s a Data Scientist’s Work Life Like?
Beyond the job itself, where you work, your salary, and your prospects matter—and data science offers great options for analytical thinkers.
Where Do Data Scientists Work?
They’re found in offices across all industries (tech, healthcare, finance, retail, etc.) and organizations (startups, corporations, nonprofits). Many work in tech hubs like Silicon Valley, New York, Seattle, or Boston, but remote work is increasingly common.
Your role can change depending on the company. Some data science teams act as consultants, helping different departments analyze data and build models. At big tech firms, you might specialize in optimizing one part of a model. Always ask about daily tasks in interviews—they vary widely.
Can Data Scientists Work Remotely?
Absolutely. All you need is a computer and data access. Many companies offer remote roles to attract talent, while others prefer in-person collaboration. The choice is yours.
What Hours Do They Work?
Most keep standard hours (8 AM–5 PM, Mon–Fri), though some companies allow flexible schedules (e.g., core hours of 10 AM–3 PM). A 40–50 hour week is typical, with plenty of autonomy. “I work 9–5, but my team values flexibility—no micromanaging,” says Nair.
What’s the Job Outlook?
Since 2012, data science has been called one of the hottest jobs. Is it still? Yes. The U.S. Bureau of Labor Statistics predicts 31% growth by 2029—much faster than the 4% average for all jobs.
How Much Do They Make?
It’s one of the highest-paying fields. The average salary in 2020 was 103,930,withseniorrolesreaching103,930,withseniorrolesreaching161,000 and directors up to 203,000.Top−levelmanagerscanearnaround203,000.Top−levelmanagerscanearnaround250,000.
What Skills Do You Need?
The two biggest skills are:
- Problem-solving with code
- Storytelling with data
If you hate coding or want someone else to define the problems, this isn’t the job for you. Data scientists are like researchers—they frame questions, test hypotheses, and iterate when things fail. Persistence is key.
Communication also matters. You’ll need to explain technical work to non-experts and convince stakeholders your solution is worth implementing. “You could be the best data scientist, but it’s pointless if no one understands your work,” Lyons’ manager once told her.
How Do You Become a Data Scientist?
Most have at least a bachelor’s degree, and many have master’s or PhDs. In fact, 80% of data scientists hold advanced degrees. But you don’t need a specific “data science” degree—backgrounds in math, computer science, or even neuroscience work.
Common paths include:
- BS in CS, stats, or math → Learn data science tools → Get hired
- Any bachelor’s degree → Complete a bootcamp → Start as a data analyst or scientist
- MS in data science or related field → Learn tools in the program → Get hired
- PhD in a data-heavy field → Learn coding for research → Internships → Get hired
The key is learning to code and solve problems with data. STEM degrees help, but bootcamps, certifications (AWS, IBM), and a strong GitHub portfolio can also open doors.
Should You Become a Data Scientist?
If you love coding, problem-solving, and constant learning, this is a great fit. If you dislike coding or prefer people-focused roles, it’s not.
One downside? Diversity in tech is lacking—only about 20% of data scientists are women, 5.8% are Black, and 3.3% are Hispanic/Latinx. If you’re from an underrepresented group, you might face being “the only one” on your team. But if biased AI models concern you, joining the field lets you advocate for fairer systems.
What’s the Future of Data Science?
With automated tools like H2O.ai and AWS Sagemaker, will machines take over? Not yet. Just like in software development, automation creates demand for more specialized skills.
A new role, the citizen data scientist, is emerging—someone without formal training who uses simpler tools to analyze data. “Companies need domain experts to solve their own problems,” says Tim Kraska of MIT. But trained data scientists will still be needed to refine models and handle complex challenges.
Even if you don’t pursue it as a career, data skills are valuable in any job. So if this isn’t for you, learning a bit about data can’t hurt.