Data Scientist
Data scientists build the models that forecast, classify and automate decisions, combining statistics, code and business context to turn raw data into working systems.
- Median salary*
- $124,800
3.9%vs last year, before tax
- People employed
- 3,400
3.0%vs last year
- Projected growth*
- +24.6%
to 2035
- AI exposure*
- Moderate
- automation risk
- Average hours*
- 41/wk
+1h vs all jobs
- Shortage status*
- In shortage
national
Data scientists work in a team alongside software engineers, analysts and the people who will use the result, in banks, insurers, government agencies, universities, consultancies and technology companies. What separates the job from a data analyst's is building predictive models and getting them into production, rather than mainly reporting on what has already happened. Individual roles lean one way or the other, toward engineering work such as pipelines and deployment or toward analysis and explanation, depending on how large the team is.
How much do data scientists earn?
The median full-time salary for a data scientist is $124,800 per annum, before tax, up $25,900 since 2018.
Pay in this field splits sharply by sector, with banks, insurers and trading firms generally paying above the median and government, education and not-for-profit employers paying less for similar work. Seniority and whether the role leans toward engineering, production systems or analysis also move the figure, and contract and consulting rates sit outside the full-time median altogether.
What does a data scientist do day to day?
The list below is what fills most weeks; the exact mix shifts with seniority and whatever stage the current work is at.
- Cleaning and reshaping messy data before any modelling can start
- Tracking down where a number in a dataset actually came from, often by asking the team that collected it
- Testing a model against real-world outcomes, not just its accuracy score
- Explaining what a model found to the people who will decide whether to act on it
- Rebuilding a pipeline because a stakeholder wants the data cut a different way
What skills do data scientists need?
Employers look for machine learning, statistical modelling, data analysis, backed by Python fluency and strong problem solving.
Specialist skills
- Machine learning
- Statistical modelling
- Data analysis
Software and tools
- Python
- SQL
- Spark
- cloud ML platforms
General skills
- Problem solving
- Presenting and data storytelling
Is the job growing?
About 3,400 people work as data scientists in Australia, and employment is projected to grow 24.6% over the decade to 2035. That's very strong growth. Few roles in Australia are expanding this fast, and it points to solid demand for years to come.
How do you become a data scientist?
Here's the path most data scientists take, step by step.
- 1Build the quantitative base
A bachelor degree in statistics, mathematics, computer science, economics or engineering is the usual starting point, and about 52% of the people working as data scientists hold one. Choose units in probability, linear algebra and programming rather than only in general information systems.
- 2Learn to code properly
Python and SQL are the working languages of the job, so you need to write code other people can read and maintain, not just run a notebook. University projects, online courses and personal projects all count here.
- 3Get your hands on real, messy data
An internship, a junior analyst role or an internal transfer into an analytics team is what turns coursework into a track record, because workplace data arrives incomplete and undocumented in ways assignment data never does.
- 4Add postgraduate study if you need it
About 26% of data scientists hold a postgraduate qualification, commonly a master's in data science or statistics. It is the usual route in for career changers from a non-quantitative degree, and a graduate diploma or certificate covers much of the same ground in less time.
- 5Show work you have shipped
Employers hiring data scientists want to see projects, code repositories or published analyses, because they are evidence you can take a vague question through to a result. Kaggle competitions and open datasets are common ways to build that without an employer's data.
Study routes we have checked
Qualifications that are a recognised way into this role, and what else you need.
- Bachelor of Business/Bachelor of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Computer Science/Master of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Mathematics/Master of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Science/Bachelor of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
- Bachelor of Science/Master of Data Science
A recognised route into this role
Complete relevant data-science coursework and projects. Data engineering also requires practical database and programming capability; employer requirements vary.
Ready to apply as a data scientist?
Whether you're working toward becoming a data scientist or already are one and want a hand with the next step (sharpening your resume for ATS screening, tightening your cover letter, or knowing what you'll actually be asked at interview), here are examples grounded in this specific role, not generic templates.
What jobs can a data scientist move to?
Moving into Quantitative Analyst typically comes with the biggest pay rise, worth $23,400 a year more on average.
| Move to | Typical pay change | Overlap | Retraining |
|---|---|---|---|
| Quantitative Analyst Quantitative modelling and programming carry into quantitative analysis, with short course study adding finance and market knowledge. | +$23,400 | 50% | short course |
| Quantitative Trader Quantitative modelling and coding skills carry into quantitative trading, with short course study adding market microstructure and risk knowledge. | +$23,400 | 50% | short course |
| Machine Learning Engineer Quantitative modelling and coding skills carry into machine learning engineering, with short course study adding production deployment. | +$2,600 | 48% | short course |
| Mathematician Mathematical modelling and computational skills carry into mathematician roles, applying theory to practical problems with little further study. | −$13,000 | 63% | minimal |
| Statistician Quantitative modelling and computational skills carry into statistician roles, adding deeper theoretical foundations through substantial study. | −$18,200 | 63% | reskill |
Moves are chosen from Jobs and Skills Australia's Data on Occupation Mobility, which follows income tax records between 2011-12 and 2020-21, together with entry requirements and skill overlap. A known move is one people were seen making in that data. Pay change compares median full-time pay for the two roles.
Who works as a data scientist?
The typical data scientist is 34 years old; 73% are men, 88% work full-time, and full-timers average 41 hours a week.
- 34
- Median age
- 27%
- Female share
- 88%
- Full-time
- +1h
- vs all-jobs avg
What's it like being a data scientist?
The job runs on long stretches of preparation followed by short bursts of modelling, then a round of explaining what you found to people who will not read your code. Much of the difficulty is in the question rather than the maths, because stakeholders often describe a symptom and leave you to work out what is actually measurable. It suits people who enjoy investigating a puzzle with no clean answer at the start and who are willing to keep asking where a number came from.
What people like
- The chase behind a number. Working out why a field is populated the way it is can mean talking to three teams and reading old documentation, and that detective work is often the most satisfying part of the week.
- Seeing a model used. A pricing rule, a fraud alert or a demand forecast that changes what someone does on Monday is the clearest payoff in the job.
- Problems with a measurable answer. You can test whether you were right, which is unusual in knowledge work and makes the feedback loop short.
- Explaining it well. Turning a technical result into something a manager can act on is a skill in its own right, and being good at it means your work gets used.
What people find hard
- Cleaning eats the week. Joining datasets, fixing inconsistent categories and chasing missing values routinely takes more time than the modelling everyone imagines you do.
- Stakeholders change the question. A request that arrives as one thing often turns out to be another once you look at what data actually exists, and the analysis restarts.
- Models that never ship. Plenty of sound work ends in a slide deck because no one owns the system that would run it in production, which is dispiriting when you built it to be used.
- The hype around the title. Expectations set by conference talks and job ads do not always match the day-to-day, which is closer to careful engineering and analysis than to invention.
Based on our synthesis of professional-body surveys and public accounts of the role, not first-person verified reviews.
Which industries employ data scientists?
Professional, Scientific and Technical Services employs the largest share of data scientists, followed by Financial and Insurance Services.
Top employing industries
- 1Professional, Scientific and Technical Services
- 2Financial and Insurance Services
- 3Public Administration and Safety
- 4Education and Training
- 5Information Media and Telecommunications
Ranked by employment share; the source doesn't publish an exact percentage per industry.
| Bachelor degree | 52% | |
|---|---|---|
| Postgraduate | 26% | |
| Diploma / Advanced Diploma | 13% | |
| Other | 9% |
Will AI replace data scientists?
AI touches data science more than most analytical jobs, because the tools being built are aimed squarely at the work: model fitting, code generation and automated data preparation. That shifts the job toward deciding what is worth building, checking whether a model is sound, and getting it into production where a real decision depends on it. The exposure is moderate rather than high because those judgement and deployment steps are where the value now sits.
Share of typical working time by exposure level
- Building and validating predictive modelsAutomated model selection handles routine fitting, but choosing the target, the features and a validation split that reflects how the model will actually be used is still a person's call.30%moderate
- Cleaning and reshaping dataCode assistants generate much of the boilerplate transformation logic, though someone still has to establish what a messy column is supposed to represent.25%high
- Deploying and monitoring models in productionManaged platforms remove much of the plumbing, but deciding when a model has drifted far enough to retrain depends on costs and risks the platform does not know about.25%moderate
- Framing the question and explaining findingsWorking out what the business is actually asking and persuading people to act on the answer relies on context and relationships that no model supplies.20%low
Common questions about becoming a data scientist
Straight answers to the questions people ask most.
How much does a data scientist earn in Australia?
The median full-time data scientist earns $124,800 per year before tax. Pay moves with sector, seniority and how much of the role is engineering rather than analysis, with banking, insurance and trading firms paying above the median for comparable work.
How do you become a data scientist?
Most people start with a quantitative degree in statistics, mathematics, computer science or economics, learn Python and SQL, and get a first role through an internship, a junior analyst position or an internal move. Postgraduate study is the common route for career changers from other fields. Employers look for evidence you have taken a real, messy dataset through to a result.
Are data scientists in demand in Australia?
Data scientists are currently in shortage nationally, and employment is projected to grow 24.6% over the decade to 2035. The role is small in absolute terms, with about 3,400 people working in it, so openings cluster in the larger cities and in organisations big enough to run a dedicated analytics team. If you are looking for work, target employers with an existing data function rather than trying to create the first one.
Will AI replace data scientists?
AI is changing the job rather than removing it, and the exposure is uneven. Code assistants now write much of the routine data preparation and model fitting, while framing the question, judging whether a model is trustworthy and getting a result into production and used remain human work. The people most exposed are those doing only the mechanical parts; the least exposed are those who understand the business problem and can communicate it.
What jobs can a data scientist move into?
Machine learning engineering is a common move for those who enjoy the production side, with a short course covering deployment and pay of $2,600 more compared with this role. Quantitative analysts in banking and trading draw on the same statistical and programming skills, pay $23,400 more, and usually need some finance knowledge on top. Data analysts are where many data scientists begin, since the querying and reporting skills transfer directly and the move brings $23,400 more.
Is data science a good career to start at 30 or later?
It is, and the field has a steady stream of entrants from other backgrounds, often through a master's or graduate diploma in data science. Your previous industry knowledge is an advantage, because much of the difficulty in the job is understanding the business problem rather than the mathematics. The main practical hurdle is getting the first role that gives you access to real data.
Related roles
- Machine Learning Engineer
- Quantitative Analyst
- Quantitative Trader
- Mathematician
- Statistician
- Data Analyst
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