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ANZSCO 2247Skill level 1Technology and data

Data scientist

Build statistical and machine-learning models to drive product and operational decisions.

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Salary

Cited figures from Job Outlook and QILT. ExamExplained does not publish predictive earnings or projections.

FigureAUDSource
Full-time weekly earnings$2500Job Outlook (2025-06-01)
Graduate starting salary$85,000QILT (2025-03-01)

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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

  1. 1Finish Year 12 with English, Maths Methods or Specialist (Specialist is strongly preferred for stats and ML)
  2. 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
  3. 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
  4. 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
  5. 5Apply for data science graduate programmes (banks, telcos, tech, Big four advisory) or start as a data or analytics engineer and pivot
  6. 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 salary bands. Salary figures are sourced from Job Outlook, QILT or industry bodies; brackets are 25th-75th percentile not absolute floors or ceilings.

  1. Graduate
    0-2 years
    Typical roles: Graduate data scientist, Junior ML engineer, Associate data scientist
    Salary band: $80,000 - $105,000 per year (source, sourced 2026-05-21)
  2. Mid-level
    3-5 years
    Typical roles: Data scientist, ML engineer, Research engineer
    Salary band: $120,000 - $160,000 per year (source, sourced 2026-05-21)
  3. Senior
    6-9 years
    Typical roles: Senior data scientist, Senior ML engineer, Applied scientist
    Salary band: $165,000 - $220,000 per year (source, sourced 2026-05-21)
  4. Lead or principal
    10+ years
    Typical 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

Three ways in

Uni, TAFE and trade routes for data scientist. Not every career has all three; 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.

Apprenticeship trade

Earn while you learn through an Australian Apprenticeship.

Not an apprenticeship trade.

Sources

ExamExplained does not publish predictive salary figures. For current Australian earnings data check Job Outlook directly. Career classifications follow the ABS ANZSCO 2022 release.