Resume structure and impact-bullet rewrites for data analysts and data scientists — proving business impact, not model metrics, with the stack visible at a glance.
The classic data-resume mistake is describing the model instead of the decision. Nobody hired an analyst to achieve 0.92 AUC — they hired them so the business would do something smarter. Every bullet should end at the decision or the money.
| Before (task) | After (decision/money) |
|---|---|
| Built dashboards in Looker | Built the retention dashboard the exec team now runs Mondays on; surfaced a churn spike that triggered a pricing fix worth ~8% of MRR |
| Analyzed A/B test results | Designed and analyzed 15+ experiments per quarter; killed two roadmap features whose "wins" were novelty effects, saving a quarter of eng time |
| Created a churn prediction model | Shipped a churn model that feeds the CS priority queue — save-rate on flagged accounts doubled vs. untargeted outreach |
| Cleaned and maintained data pipelines | Rebuilt the ingestion pipeline, cutting daily refresh failures from weekly firefights to near zero and restoring team trust in the numbers |
The formula: analysis → what the business did differently → measurable consequence. If a project changed no decision, either find the decision it informed or leave it off.
Not legal advice
This template is provided for general informational purposes only and is not legal advice. Laws differ by jurisdiction and change over time — have a qualified professional review any document before you rely on it.
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