Data Scientist CV example
Experienced · UK CV · set in the Data & AI template
Data science CVs fail most often by describing methods without outcomes. A hiring manager wants the business question, the approach in a phrase, and what changed because the model shipped. Statistical judgement shows in how honestly results are reported.
A sample person: the name, employers and figures are invented. A data scientist with three years in consumer fintech, building pricing and churn models that shipped, moving to a larger product team.
The order, and why
A one-line profile; experience as the main block; selected projects; education; skills grouped by type.
Weak bullets, made strong
Weak: Developed machine learning models to predict churn
Strong: Shipped a churn model (gradient boosting, 0.81 AUC) driving a retention offer that reduced monthly churn from 4.1% to 3.4% in an A/B test
Problem, method, accuracy and the measured business result.
Weak: Created dashboards for stakeholders
Strong: Replaced 14 manual reports with one self-serve Looker model, saving the finance team 2 days a month
Specific, counted and tied to someone who benefited.
Mistakes to avoid
- Accuracy figures with no baseline or test design.
- Listing every library rather than the work.
- No sign that anything reached production.
UK CV or US resume
UK: a CV of one or two pages is normal at this level. US: one page, with the same structure and outcome-led bullets.
Live data science roles
Open now on the tracker, with their deadlines.
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