Career objective
I bring [Data science background or relevant analytical experience] and [Relevant statistical, programming, and evaluation skills]. I am seeking [Target data science role and subfield] to support [Research questions or business decisions you aim to support].
Copy-ready template
[Your Full Name] [City, Region] | [Email Address] | [Phone Number] | [Portfolio or Project Repository URL] PROFESSIONAL SUMMARY [Data science or analytical background] with experience investigating [Problem type] using [Methods]. Skilled in [Programming language], [Statistical technique], and [Evaluation approach]. [Verified analytical contribution with relevant limitations]. SKILLS [Statistical analysis] | [SQL querying] | [Data cleaning] | [Predictive modelling] | [Experiment analysis] | [Model evaluation] | [Reproducible analysis] PROFESSIONAL EXPERIENCE [Job Title] | [Company Name] | [Start Date–End Date] - Prepared [Dataset] using [Cleaning or validation method], documenting [Quality issue and treatment]. - Applied [Statistical or modelling method] to [Question], comparing results against [Baseline or alternative]. - Communicated [Finding] through [Report or visualization], explaining [Uncertainty or limitation] to support [Decision]. EDUCATION [Degree or Relevant Education] [School Name] | [Completion Date or Expected Completion Date]
How to tailor it by subfield
- Machine-learning modelling: Highlight baseline selection, feature preparation, validation strategy, and error analysis. Explain how evaluation reflected the intended use and avoided data leakage.
- Business analytics and experimentation: Emphasize metric definitions, experimental design, statistical analysis, and decision support. Distinguish observed associations from conclusions supported by experimental evidence.
- Natural-language or computer-vision applications: Describe the text or image task, data preparation, evaluation approach, and relevant error categories. State your actual contribution to labelling, modelling, or analysis.
- Data engineering-adjacent analysis: Focus on querying datasets, investigating data quality, validating transformations, and documenting analytical requirements. Describe collaboration on data workflows according to the work you performed, without implying engineering credentials or responsibilities you did not hold.
Common mistakes
- Reporting model performance without naming the metric, baseline, or evaluation setup.
- Allowing information from evaluation data to influence training or feature preparation.
- Presenting correlations as evidence of causation.
- Claiming production deployment or data infrastructure ownership when your work covered analysis only.
- Omitting data quality limitations or uncertainty from analytical conclusions.
Sample resume
SAMPLE — all names, companies, and schools below are placeholders.
[Your Name] [Email Address] | [Phone Number] | [Project Repository URL] PROFESSIONAL SUMMARY [Analytical background, methods used, and supported contribution] SKILLS [Querying skill] | [Statistical method] | [Evaluation skill] PROFESSIONAL EXPERIENCE [Job Title] | [Company Name] | [Dates] - [Dataset analyzed, quality checks performed, and question investigated] - [Method evaluated, baseline comparison, and finding with limitations] EDUCATION [Degree or Relevant Education] | [School Name] | [Date]
Key skills to highlight
- SQL joins, aggregations, and window functions
- Data cleaning and quality validation
- Exploratory data analysis and visualization
- Statistical inference and uncertainty estimation
- Experiment design and treatment-effect analysis
- Feature preparation and leakage prevention
- Model validation and baseline comparison
- Reproducible analytical scripts and documentation
Cover letter companion
Dear [Hiring Manager Name], I am applying for the [Role Title] position at [Company Name]. My experience includes [Relevant analysis or modelling project]. In that work, I used [Method] to establish [Supported finding]. Thank you for considering my application; I would welcome the opportunity to discuss my contribution. Sincerely, [Your Name].