Launch your data scientist career
Break into data science with the skills employers want. Learn how to enter the industry, switch fields, or advance your career as a data scientist.
What does a data scientist do?
Data science is a dynamic field that blends research, engineering, and communication to build predictive models, identify patterns, and generate insights to inform business decisions.
As a data scientist, you will identify valuable data and collect, clean, and organize it. You will write code, apply statistics, and use machine learning to predict trends, optimize processes, create products, or inform strategy.
You will present your findings to stakeholders through data visualizations like graphs and live dashboards. Most data scientists work on cross-functional teams, and the role isn't one-size-fits-all. In entertainment, fintech, healthcare, or manufacturing, if strategy depends on data, data scientists are behind it.
| Job growth (2023–33) | +36% |
|---|---|
| Projected new jobs per year | 20,800 new jobs |
Your path to becoming a data scientist
For career starters
Wondering how to become a data scientist? Here's what to focus on:
- Start with an introduction to data science course and learn the basics: statistics, Python, SQL, and Tableau fundamentals.
- Earn a bachelor's in data science or a similar field. Not ready to commit to a full degree? Earn college credit while learning the fundamentals with a MicroBachelors® program.
- Build a portfolio of work, and apply for internships before graduating to get hands-on experience.
For career starters
Wondering how to become a data scientist? Here's what to focus on:
- Start with an introduction to data science course and learn the basics: statistics, Python, SQL, and Tableau fundamentals.
- Earn a bachelor's in data science or a similar field. Not ready to commit to a full degree? Earn college credit while learning the fundamentals with a MicroBachelors® program.
- Build a portfolio of work, and apply for internships before graduating to get hands-on experience.
Programs that can prepare you for a data scientist role
What do you need to learn to work as a data scientist?
Essential technical skills
- Programming languages (Python, R)
- SQL and database querying
- Machine learning (TensorFlow, PyTorch)
- Statistical analysis and modeling
- Data visualization tools (Tableau, Power BI)
Soft skills
- Critical thinking and problem-solving
- Written and verbal communication skills to explain findings to nontechnical stakeholders
- Collaboration
- Domain (industry) knowledge
- Business acumen
Required education
- Bachelor's degree in data science, computer science, statistics, or mathematics
- Depending on the role, employers may prefer candidates with a master's or doctoral degree
- Azure, AWS, or TensorFlow certifications
"My advice for those who want to become data scientists would be to go deeper and truly understand the algorithms, statistics, and modeling behind data science. Understanding how models operate and how to work with data, how the data behaves, and how it ties to the business is what makes you a great data scientist."
— Juan Figueroa, data scientist manager at Xtillion
Data scientist industry insights
Key takeaways
- Competitive pay across industries
- High demand in sectors beyond tech
- Transferable skills in artificial intelligence (AI), machine learning, and business
- Clear pathways for growth and specialization
Demand for data scientists is growing fast. According to the BLS, positions for these professionals are projected to grow 36% between 2023 and 2033, much faster than the average for all occupations.
And that growth isn't just coming from tech companies. Industries like healthcare, manufacturing, aerospace, and finance are racing to implement AI and machine learning automation and develop new products, additional revenue streams, and market trend forecasts.
Holly Lee, a certified career coach and former global recruiting leader for Amazon, Google, Facebook, and Microsoft, notes that data scientists are among the professionals driving AI adoption across industries.
Industries like aerospace, healthcare, financial services, and manufacturing are huge right now. I'm trying to help people have an open mind to step into those spaces, because that's where many job openings are.
While data science roles are in demand nationwide, salaries and opportunities vary widely by location. For example, Lee points out that Phoenix, Arizona, has a strong demand for aerospace, not traditional tech, but still offers solid career potential. In contrast, markets like Silicon Valley, Seattle, Austin, and the East Coast have a dense concentration of tech roles, often with higher salaries.
Now, data scientists are in high demand by employers, shared Lee. And they will pay for that talent,
she added.
When evaluating data scientist jobs, Lee suggests you ask yourself:
- What industry am I interested in or open to working in?
- Where is that industry concentrated?
- What's the cost of living in that location?
- How does the local job market look for data talent?
Top 5 paying industries for data scientists
| Industry | Annual median wage (May 2024) |
|---|---|
| Taxi and rideshare service companies | $206,170 |
| Streaming, media, and social media organizations | $172,280 |
| General merchandise retailers | $164,350 |
| Web search portals, libraries, archives, and other information services | $164,320 |
| Software publishers | $161,890 |
Explore top data scientist salaries by state
Get a detailed look at the top five highest-paying states and see how all 50 rank in our data scientist salary by state guide.
Data scientist career track
Data science is a booming industry, one of the few projected to remain stable for workers despite the widespread adoption of AI.
"Even if jobs are getting replaced by AI, who builds the AI and the robots? Data scientists do. If you want to have a secure job, I'd recommend anything involving AI and machine learning, across all industries."
— Holly Lee, certified career coach and former global recruiting leader
As you grow in your data science career, developing skills in AI, machine learning, and business strategy, along with deep domain knowledge, can unlock new job opportunities and set you on a clear path to advancement.
Some data scientists pivot into focused areas like machine learning or R&D. Others follow a more linear path with time, experience, and strategic skill-building.
Below is the common career track for data scientists, from entry-level to senior leadership jobs, according to Lee:
Early career roles
(0–2 years)
- Data science intern
- Junior data scientist
- Business intelligence analyst
- Data analyst
- Data engineer
Mid-career roles
(2–5 years)
- Analytics manager
- Data scientist
- Machine learning engineer
- Data engineer
Senior career roles
(5–10 years)
- Data science manager
- Head of data science
- Lead data scientist
- Senior data scientist
- Staff engineer
- Staff data scientist
Leadership & C-suite
(10+ years)
- Chief artificial intelligence officer
- Chief analytics officer
- Chief data scientist
- Senior director of data science
- Vice president of data science
Ready to advance your career?