Data Scientist resume example

This example resume shows how a mid-level data scientist in Manchester can present modeling, experimentation and stakeholder work with measurable outcomes. It balances technical depth with business impact.

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Alex Morgan

Data Scientist

Manchester

Professional summary

Data scientist with five years of experience building churn, forecasting and experimentation solutions in Python and SQL. Translated ambiguous business questions into measurable models and clear recommendations. Committed to well-documented, reproducible analysis.

Experience

Data Scientist · Northgate Retail Analytics
2022 – Present
- Built a gradient-boosted churn model in scikit-learn that helped the retention team target outreach, lifting campaign response rates by 11%
- Designed and analyzed A/B tests on checkout changes, turning results into launch or rollback recommendations for product managers across four releases
- Automated a weekly demand forecasting pipeline in Python and SQL, cutting manual reporting effort by about six hours per week
Junior Data Analyst · Brightwater Insights
2019 – 2022
- Wrote SQL queries to join warehouse tables for ad hoc requests, reducing average turnaround from three days to one
- Developed Tableau dashboards tracking sales and returns, adopted by 25 regional managers for weekly reviews
- Trained baseline regression models to estimate delivery times and documented results for peer review, reducing forecast error by 8%

Skills

Python · SQL · Machine Learning · Statistical Modeling · A/B Testing · Feature Engineering · scikit-learn · Pandas · Data Visualization · Tableau

Education

BSc, Mathematics and Statistics — University of Manchester (2019)

This example is fictional and written for illustration. Replace every name, company, date and number with your own facts.

Lead each bullet with the model or analysis you built, then state the business metric it moved, such as response rate, forecast error or hours saved.
Name the techniques and libraries inside bullets, like gradient boosting or scikit-learn, so recruiters and applicant systems can match your skills to the role.
Show experimentation rigor by mentioning how many tests you ran and what decisions followed, rather than only saying you know A/B testing.
Describe collaboration with product, marketing or operations teams to show you can frame vague questions as measurable problems.
Include one line on deployment or reproducibility, such as automated pipelines or documented code, to separate production work from notebook experiments.

Should a data scientist resume include projects?

Yes, if they are relevant and you can describe the data, method and result. They matter most for early-career candidates, but a short link to a documented project can help at any level.

How technical should the bullets be?

Mention the key methods and tools, but pair each with a business outcome. Readers in recruiting may not know the algorithms, while hiring managers want evidence of the technical choices you made.

How long should the resume be?

One page is usually enough for up to about five years of experience, and two pages for longer careers. Prioritize recent roles and results that match the job description, since length alone does not decide outcomes.