What is a data scientist?

Data scientists are problem-solvers whose toolkits are full of code, curiosity, and critical thinking. They turn messy, complex information into clear insights that drive intelligent business growth. 

Data scientists are behind the everyday decisions that drive innovation. From anticipating customer needs to helping teams act with confidence, they’re paving the way forward. 

In this guide, we’ll explore what a data scientist is, the skills that set them apart, and how to build a career in this fast-growing field. 

Key points 

• Data scientists turn raw data into insights that guide decisions, predict trends, and drive innovation. 
• Key skills include coding, statistics, data visualization, and business storytelling. 
• Data science specializations range from machine learning to business intelligence. There’s a niche for every passion. 
• Unlike data analysts, data scientists focus on prediction and experimentation, not just reporting. 
• You don’t need a Ph.D. to get started in data science. A strong portfolio and hands-on experience can take you a long way. 

What does a data scientist do?

Data scientists turn raw data into real-world value. They dig through massive datasets, find patterns, and translate those findings into actionable insights that teams can use. Their work fuels everything from personalized product recommendations and fraud detection to business forecasting. 

While day-to-day duties vary depending on the industry, most data scientists tackle a mix of these responsibilities: 

• Cleaning and organizing data so it’s accurate, consistent, and ready for analysis 
• Building models that predict trends or behavior 
• Analyzing experiments to test ideas and guide decisions 
• Visualizing results through charts and dashboards that tell a clear story 
• Collaborating across teams—from marketing to engineering—to put insights into motion 
• Communicating findings in a way that’s digestible, compelling, and actionable 

In short, data scientists connect the dots between information and impact, helping businesses move with clarity, not guesswork. 

Key skills and tools used by data scientists

Great data scientists know what to look for (and know how to look for it). Their skill set blends technical know-how with sharp communication and a strong sense of curiosity. 

Here are the skills and tools they typically lean on: 

• Programming skills: Code is central to data science. Languages like Python, R, and SQL are the building blocks of exploring data, testing hypotheses, and building custom models. 
• Statistical knowledge and machine learning: From A/B testing to building recommendation systems, data scientists use statistics and algorithms to spot trends and make predictions. 
• Data visualization: Communicating findings clearly matters just as much as the math behind them. Tools like Tableau, Power BI, matplotlib, and Seaborn help turn raw numbers into visuals that tell a compelling story. 
• Business acumen and storytelling: It’s easy to get lost in the numbers when you’re mining for insights all day. Once you strike gold, you need to make those insights resonate with other stakeholders and departments. A thorough grasp of business operations and storytelling helps you land your message. 
• Collaboration and communication: Data science is a team sport. Sharing ideas, giving updates, and aligning with different departments helps bring projects to life. 

Types of data scientists and specializations

Not all data scientists wear the same hats. While a core skill set translates across specialties, many lean into niches that match their strengths and passions.  

Here are a few of the most common data science roles: 

Machine learning engineer

Machine learning engineers design and build algorithms that can learn from and make predictions based on data. Their models power everything from recommendation engines on streaming platforms to fraud detection tools in banking apps.  

Data scientists work closely with software engineers in these roles and often deploy models into production at scale. To thrive as a machine learning engineer, you’ll need strong coding skills, a deep understanding of math and statistics, and experience with frameworks like TensorFlow and PyTorch. 

Natural language processing (NLP) specialist

NLP specialists operate at the intersection of data science and human language. Their goal is to teach machines to understand, interpret, and generate language in a meaningful way.  

If you’ve ever used a chatbot or dictated a message to your phone, you’ve experienced the work of NLP professionals. This specialization blends data science, linguistics, and AI to build tools like sentiment analysis engines, language translators, and voice assistants. 

Data engineer

Data engineers are the architects of data infrastructure. They ensure that data is accessible, reliable, and primed for analysis by building and maintaining pipelines that move data from raw sources to usable formats. A deep understanding of database systems and cloud platforms is typically a must to excel in this role. 

Business intelligence (BI) data scientist

BI-focused data scientists bridge the gap between raw data and decision-making. They use tools like Tableau, Power BI, and Looker to breathe visual life into data trends. They also create dashboards that help business leaders take action.  

This role blends data analysis with storytelling, so strong communication skills are just as important as technical ones.  

Other specializations

As data science grows, so do the potential job opportunities. Some roles focus on deep learning, training neural networks to recognize images, speech, or patterns. Others dive into computer vision, enabling machines to “see” and interpret visual inputs. In product-focused companies, product data scientists use data to inform feature development, user experience, and customer retention strategies.  

From AI to UX, there’s a growing number of ways to specialize in data science. Whatever your interest, chances are there’s a path that fits. 

How data scientists differ from data analysts

The titles sound similar—and the two roles work together—but data scientists and data analysts have different goals, tools, and levels of technical depth. 

Here’s how they stack up: 

Area	Data scientist	Data analyst
Main focus	Prediction, modeling, experimentation	Reporting, dashboarding, descriptive analysis
Scope of work	Tackles open-ended problems using advanced techniques	Answers defined business questions with structured data
Tools and tech	Programming (Python, R), machine learning, cloud computing	SQL, Excel, BI tools like Tableau and Power BI
Skills required	Coding, statistics, data engineering, algorithm design	Strong analytical skills, data visualization, business knowledge
Output	Predictive models, algorithms, experimental analysis	Charts, reports, and dashboards that explain historical data
Collaboration style	Works with engineers, product managers, executives	Often supports marketing, ops, finance, sales teams

In essence, data analysts explain the “what,” while data scientists go after the “why” and “what’s next.” Both roles are critical, but if your business is ready to move from reactive to proactive, working with data scientists can help you reach that goal. 

How to become a data scientist

There’s no single path to becoming a data scientist. Some come from formal academic backgrounds, while others take self-taught, project-driven routes. The key is to build knowledge and experience, showing that you can solve real problems with data. 

Choose the appropriate education or learning path

Many data scientists start with degrees in computer science, statistics, or related fields. But it’s not the only route. Bootcamps, online courses, and self-paced learning platforms can also get you there. 

While master’s and Ph.D.s are common, especially for research-heavy roles, many working data scientists don’t need advanced degrees. What matters most is your ability to think critically, write clean code, and work effectively with data. 

Build a portfolio

A strong portfolio often speaks louder than a resume. Contribute to open-source projects, write about your work, or showcase your skills through data storytelling and dashboards. Real-world examples speak volumes. If you don’t have work to show off yet, you can find portfolio projects online that will help you apply (and showcase) your skills.  

Gain experience through an internship or entry-level job

Practical experience helps bridge the gap between learning and doing. Entry-level analyst roles or internships in data-driven teams can help you get your foot in the door. 

Continue learning

Stay current on industry trends by joining online communities, reading research papers, earning certifications, or exploring tools like cloud platforms and auto machine learning (AutoML). Online platforms like Udemy and Coursera, or trade organizations like the Data Science Council of America (DASCA), are great resources for furthering your education.  

Career path and growth opportunities for data scientists 

Your career in data science will grow along with your skills, and the possibilities stretch far beyond writing code or running models. Here’s what a typical path might look like: 

1. Junior data scientist: Early-career scientists focus on learning core tools, supporting senior team members, and handling well-defined problems. This is where you build a foundation for turning raw data into actionable insights. 
2. Data scientist: With experience, you’ll take on more complex problems. You might own end-to-end projects, experiment with modeling techniques, and work more directly with stakeholders. 
3. Senior data scientist: Now you’re leading initiatives. Senior scientists guide strategy, mentor junior teammates, and often partner closely with leadership to align data work with business goals. 
4. Principal data scientist or head of data: These roles are as much about vision as execution. You may lead a team, define best practices, and shape how data science operates at the organizational level. This is where technical depth meets leadership. 

Depending on your interests, you could branch into machine learning research, product leadership, or even entrepreneurship. Whatever direction you take, this is a field that rewards curiosity, leadership, and continual learning. 

That curiosity and continual growth? It’s being rewarded in job outlook and pay. The US Bureau of Labor Statistics (BLS) projects employment of data scientists to grow 36 percent from 2023 to 2033. That’s well above the average for all occupations. And, according to the agency, the median annual pay for data scientists is $112,590. 

Become part of a data-driven future

Every day, data scientists help businesses make smarter decisions and better products. As the world leans more toward automation, personalization, and predictive insights, the need for sharp, curious thinkers who can work with data will only grow. 

If you’re excited by the idea of solving real problems with data and want to shape what’s next in tech, business, or beyond, this path is for you. And when you’re ready to take the next step, explore open data science jobs at Intuit and put your data skills to work solving real problems that make a difference for millions.