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Data scientist jobs are among the better-paid careers in technology, but the role is more than just working with numbers. Data scientists use statistics, programming, machine learning and business knowledge to turn large amounts of data into information that companies can actually use.
In the United States, the median annual salary for data scientists was $120,230 in May 2025, according to the U.S. Bureau of Labor Statistics (BLS). Employment is projected to grow 35% from 2025 to 2035, with about 24,800 openings expected each year.
The career is also well established in other major markets, although salaries, qualifications and job titles vary by country.
Data Scientist Salary Around the World
| Country | Current salary information |
|---|---|
| United States | $120,230 median annual salary |
| United Kingdom | £32,000 starter to £83,000 experienced |
| Canada | C$46.15 per hour median |
| Australia | Official current profile available, but salary data is not yet included in the new OSCA profile |
The UK National Careers Service lists salaries from £32,000 for starters to £83,000 for experienced data scientists.
Canada’s Job Bank reports a national median wage of C$46.15 per hour, with a reported range of C$30.00 to C$69.74 per hour. The figures use a 2023–2024 reference period.
Australia has recently moved to the Occupation Standard Classification for Australia (OSCA). Jobs and Skills Australia currently lists data scientist as a separate occupation, but its new profile is initially based on 2021 Census data and does not yet provide a current median salary figure.
Salary can vary substantially depending on experience, industry, location, education and whether the role focuses on areas such as machine learning, artificial intelligence or business analytics.
What Does a Data Scientist Do?
A data scientist takes messy or complex data and tries to find useful patterns in it.
Typical work can include:
- Collecting and cleaning datasets
- Analyzing large amounts of structured and unstructured data
- Building statistical models
- Developing machine learning models
- Testing and improving predictive models
- Identifying trends and relationships in data
- Creating data visualizations
- Explaining findings to business and technical teams
- Using data to support business decisions
The Australian occupation profile describes data scientists as professionals who create algorithms and statistical models and build or deploy analytics frameworks such as machine learning systems.
The actual job can look quite different depending on the employer. A data scientist at a bank may work on fraud detection or credit risk, while someone at a technology company might work on recommendation systems, customer behavior or AI products.
Skills You Need for Data Scientist Jobs
Strong technical skills are important, but successful data scientists also need to understand the problem behind the data.
Programming
Python is one of the most useful languages to learn. R is also used in statistics and data analysis.
SQL is particularly important because much of the work involves retrieving and manipulating data stored in databases.
Statistics and Mathematics
You don’t need to be a mathematician, but you should understand concepts such as:
- Probability
- Statistical testing
- Regression
- Distributions
- Correlation
- Experimental design
A solid statistical foundation becomes increasingly important when you move beyond basic data analysis into predictive modelling and machine learning.
Machine Learning
Data scientists commonly work with machine learning techniques to build models that can identify patterns and make predictions.
You should understand the difference between training and testing data, model evaluation, overfitting and feature selection before moving into more advanced techniques.
Data Visualization
Being able to explain a finding is almost as important as finding it.
Tools such as Power BI, Tableau, Python visualization libraries and other business intelligence platforms can help turn technical analysis into something decision-makers can understand.
Communication
A technically excellent model is not very useful if nobody understands what it means.
Data scientists often need to explain their findings to managers, executives, engineers and other teams that may not have a technical background.
What Qualifications Do You Need?
A bachelor’s degree is the most common entry route in the United States. BLS lists mathematics, statistics, computer science and related fields among the typical educational backgrounds for data scientists. Some employers prefer or require a master’s or doctoral degree.
In Canada, Job Bank says the occupation usually requires a university degree, which can be at the bachelor’s, master’s or doctoral level.
The UK offers more flexibility. The National Careers Service lists university study, apprenticeships, direct applications and Civil Service training as possible routes into data science. Relevant degrees include mathematics, statistics, data science, computer science and operational research.
So there isn’t one universal qualification that guarantees a data scientist job.
Can You Become a Data Scientist Without a Data Science Degree?
Yes. A data science degree is not the only route into the profession.
Someone with a degree in mathematics, statistics, computer science, engineering, physics or another quantitative subject may already have much of the foundation needed.
People from other backgrounds can also transition into the field by building skills in programming, statistics, databases and machine learning. The UK National Careers Service specifically notes that graduates from other subjects can use postgraduate conversion courses to move into AI and data science.
The difficult part is demonstrating that you can actually work with data.
A portfolio containing realistic projects can help. For example, you could analyze a public dataset, build a predictive model, create a dashboard or investigate a business problem and explain your findings.
Data Scientist vs. Data Analyst
These roles overlap, but they are not identical.
Data analysts generally focus more on examining existing data, identifying trends and producing reports or dashboards that help organizations make decisions.
Data scientists typically work further into statistical modelling, predictive analytics and machine learning.
There is also significant overlap in tools. Both may use SQL, Python, statistics and visualization platforms.
For someone starting out, data analyst roles can sometimes provide a practical entry point before moving toward more advanced data science work.
Data Scientist vs. Data Engineer
Data scientists and data engineers work closely together but solve different problems.
A data engineer focuses heavily on the systems that collect, store, transform and deliver data.
A data scientist uses that data to perform analysis, build models and generate insights.
In practice, the boundary between roles can vary by company. Smaller organizations may expect one person to handle responsibilities that would be split between several specialists at a larger company.
Australia’s current OSCA classification recognizes Data Analyst, Data Architect, Data Engineer and Data Scientist as separate occupations within its Data Professionals group.
How to Become a Data Scientist
A practical path looks something like this:
1. Build your mathematics and statistics foundation.
Focus on probability, statistics and the mathematical concepts used in modelling.
2. Learn Python and SQL.
These are among the most useful technical skills for working with real-world datasets.
3. Learn data analysis.
Practice cleaning datasets, exploring information and finding meaningful patterns.
4. Move into machine learning.
Learn how common supervised and unsupervised learning methods work and, more importantly, when to use them.
5. Build projects.
Work with real datasets rather than relying entirely on tutorials.
6. Learn to explain your results.
Your portfolio should show not just the model you built, but the problem you were solving and what the results mean.
7. Apply for relevant roles.
Depending on your background, that might mean data analyst, junior data scientist, analytics or machine learning positions.
Where Do Data Scientists Work?
Data science is used across a wide range of industries.
Common employers include:
- Technology companies
- Banks and financial institutions
- Insurance companies
- Healthcare organizations
- Retail companies
- Consulting firms
- Government agencies
- Manufacturing companies
- Telecommunications companies
- Research organizations
Australia’s current occupation data shows data scientists working across professional and scientific services, financial and insurance services, public administration, education, information media and telecommunications, retail and other industries.
That broad industry demand is one reason the career can offer several different directions as experience grows.
Data Scientist Career Path
A typical career can progress from junior or entry-level data work into positions such as:
Junior Data Scientist → Data Scientist → Senior Data Scientist → Lead/Principal Data Scientist
Some professionals move into specialized areas such as:
- Machine learning
- Artificial intelligence
- Natural language processing
- Computer vision
- Risk analytics
- Product analytics
Others eventually move toward management, strategy, consulting or research.
The skills also overlap with neighboring careers, which makes data science a useful foundation for people who later want to move into machine learning engineering, data engineering, analytics or AI-focused roles.
Data Scientist Job Outlook
The U.S. outlook is particularly strong. BLS projects 35% employment growth between 2025 and 2035, considerably faster than the average for all occupations, with about 24,800 openings per year over the period.
That growth reflects the increasing use of data, predictive modelling and AI-based systems across businesses and government organizations.
Other countries are developing their own data and AI workforce classifications as these occupations become more established. Australia’s move to OSCA, for example, now separately identifies data scientists rather than grouping them into a broader category.
For someone considering the field, the strongest long-term advantage is not simply learning a list of software tools. It is developing the ability to combine statistics, programming, machine learning and business problem-solving.
Data scientist jobs can pay well and offer strong career growth, but they also require a meaningful technical skill set. For people who enjoy working with numbers, technology and complex problems, it can be one of the more attractive career paths in the modern technology market.