Data Scientist Resume Example
This data scientist resume shows what real ML work looks like: dealing with incomplete data, convincing stakeholders, deploying models that don't break, and measuring whether any of it actually helped.
The experience section includes specific model performance metrics (AUC, precision/recall tradeoffs) alongside business impact. It also acknowledges the unglamorous parts: data cleaning, pipeline maintenance, and models that didn't work out.
Adapt this by adding your actual algorithms, the real size of your datasets, and honest metrics. If your model improved conversion by 2.3%, that's more believable than 'significantly improved engagement'.
Highlights
- Built fraud detection model improving precision from 87% to 94.2% at constant recall
- Recommendation system generating $1.8M incremental annual revenue
- Experience with full ML lifecycle: data pipelines, training, deployment, monitoring
Tips to adapt this example
- Specify dataset sizes when impressive (50M events, 10TB of data)
- Mention the business context: who uses the model, how often, what decisions it informs
- If you've deployed models to production, emphasize that—many data scientists haven't
Keywords
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