Home Data Scientist Resume template
Data Scientist resume template
A clean starting structure with example content grounded in what data scientists actually do day to day, not generic filler. Download and replace the bracketed placeholders with your own details.
Which resume format should you use?
Reverse chronological
You have a steady work history. This is the format almost every recruiter and ATS expects by default.
Functional
You're changing fields or have gaps in your employment. Leads with skills rather than a job-by-job timeline.
Combination
You're early career or have worked consistently but for only a few employers. Blends a skills summary with a shorter chronological history.
This template uses the reverse chronological format: the one most data scientists should default to, since most Australian recruiters and ATS software expect it.
Professional summary
Data scientist who builds and validates machine-learning models, then works with engineering teams to get them into production. Comfortable moving between statistical rigour and plain-language explanations for non-technical stakeholders, with a track record of designing experiments that hold up under scrutiny.
Key skills
- Machine learning
- Statistical modelling
- Data analysis
- Python
- SQL
- Spark
- Cloud ML platforms (AWS/Azure/GCP)
- Experiment design and A/B testing
- Feature engineering
- Data storytelling and presenting
Experience: example bullet points
- Built and validated machine-learning models for [use case], improving prediction accuracy against the existing baseline
- Designed and ran controlled experiments to measure the impact of [change], including sample size and significance checks before rollout
- Engineered features from raw transactional and behavioural data, reducing model training time by streamlining the pipeline
- Worked with engineering teams to deploy models into production, including monitoring for drift and retraining triggers
- Presented model behaviour and limitations to non-technical stakeholders, translating outputs into decisions the business could act on
Education
Bachelor degree in data science, statistics, computer science or a related quantitative field is the most common entry point, with a growing share holding postgraduate qualifications (master's or PhD) for research-heavy or senior roles.
Keywords an ATS is likely to scan for
Applicant tracking systems match your resume against terms in the job ad before a person ever sees it. Only include the ones that actually apply to your experience, but if a term below matches something you've done, use the same wording the job ad uses.
- Machine learning
- Statistical modelling
- Python
- SQL
- Spark
- MLOps
- Cloud ML platforms
- A/B testing
- Feature engineering
- Data pipelines
- Model deployment
- Data governance
- Predictive analytics
- Role: Data Scientist
Getting past ATS screening
- Match the specific skills, certifications and terms used in the job ad, not just your own wording for the same thing.
- Keep formatting simple: no tables, text boxes, columns, headers/footers or graphics. Parsers frequently drop content placed in these.
- Submit as .docx or PDF unless the job ad specifies otherwise.
- Use standard section headings (Experience, Education, Skills) rather than creative alternatives.
- List your core skills and technical competencies in their own section so a keyword scan can find them instantly.
This is a starting point, not a guarantee of interviews. Tailor every bullet point to your own real experience and the specific job ad.