Data scientist
Build statistical and machine-learning models to drive product and operational decisions.
Salary
Cited figures only: median full-time weekly earnings for the ANZSCO unit group Actuaries, Mathematicians and Statisticians (2241) from Your Career (Jobs and Skills Australia), and where available the QILT graduate starting salary for the matching study area.
| Figure | AUD | Source |
|---|---|---|
| Graduate starting salary | $80,000 | QILT (2026-08-31) |
What a data scientist actually does
Data scientists run a longer cycle than analysts. Most days are spent in a Python notebook (Jupyter or VS Code), a SQL editor and Git. Mornings often start with a stand-up plus a check on any running training jobs or scheduled pipelines. From there the work blocks into framing a problem with a product partner, pulling and cleaning data, prototyping models in scikit-learn, PyTorch or LightGBM, and validating results against a hold-out set. Afternoons split between writing up findings, code review on teammates' pull requests, and pairing with data engineers or ML engineers on shipping a model to production. The job involves a lot of reading: paper review for new techniques, internal experiment write-ups, and stakeholder docs. Hours are typically 38-45 per week, hybrid with 2-3 office days, with peaks around quarterly model reviews and incident response when a deployed model misbehaves.
Typical tasks
- Engineer features from raw data.
- Train and validate models.
- Deploy models to production with engineering teams.
Skills you'll use
- Python (NumPy, pandas, scikit-learn, PyTorch or TensorFlow)
- SQL across both warehouse and analytical engines
- Statistics including hypothesis tests, regression and Bayesian basics
- Causal inference and A/B test design
- Git, code review and basic software engineering hygiene
- Cloud ML tooling (SageMaker, Vertex AI, Azure ML or Databricks)
- Writing clear technical narratives for non-data audiences
- Reading research papers and translating them into shippable code
How to become one
- 1Finish Year 12 with English, Maths Methods or Specialist (Specialist is strongly preferred for stats and ML)
- 2Complete a 3-4 year Bachelor of Data Science, Bachelor of Mathematics, Bachelor of Computer Science, or honours-level science with a strong quantitative core
- 3Build a portfolio of 2-3 substantive projects on GitHub. Include a model trained end-to-end, an experiment writeup, and a short blog post explaining your reasoning
- 4Most data scientists do a postgraduate qualification (Master of Data Science, Master of Statistics) or honours. PhD is common for research roles but not required for industry
- 5Apply for data science graduate programmes (banks, telcos, tech, Big four advisory) or start as a data or analytics engineer and pivot
- 6Specialise by year 3-5 in NLP, computer vision, recommender systems, forecasting, MLOps, or causal inference
Where you can work
- Big four banks, insurers and superannuation funds
- Australian arms of large global tech and AI product companies
- SaaS scale-ups and digital product companies
- Federal agencies including ABS, ATO, CSIRO and Defence Science
- Universities and the medical research institutes
- Consultancies including Big four advisory data and AI teams
- Telcos, large retailers and online marketplaces
Career progression
Typical stages and roles. Years of experience are a rough guide; pay at each stage depends on the employer, award or agreement.
- Graduate0-2 yearsTypical roles: Graduate data scientist, Junior ML engineer, Associate data scientist
- Mid-level3-5 yearsTypical roles: Data scientist, ML engineer, Research engineer
- Senior6-9 yearsTypical roles: Senior data scientist, Senior ML engineer, Applied scientist
- Lead or principal10+ yearsTypical roles: Lead data scientist, Principal data scientist, Head of data science
Is this for you?
You might love this if
- You like sitting with a fuzzy problem until you can frame it precisely
- You're comfortable with the maths behind common ML techniques
- You can explain a model's limits without sounding defensive
- You read papers, blogs or post-mortems for fun
- You're willing to do the boring data cleaning before the interesting modelling
This might not suit you if
- You want fast, certain answers rather than careful uncertainty
- You dislike rigorous testing, code review and writing things down
- You expected mostly modelling and not much data cleaning, in reality cleaning is most of the job
- You're not interested in the business or scientific context behind the data
Ways in
Uni and TAFE routes for data scientist. We only list pathways that actually lead to this occupation.
University
Bachelor degrees that lead to this career.
TAFE / VET
Nationally accredited Certificate and Diploma qualifications.
No direct TAFE pathway to this career.
Sources
ExamExplained does not publish predictive salary figures. For current Australian earnings data check Your Career (yourcareer.gov.au) directly. Career classifications follow the ABS ANZSCO 2022 release.