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What this role's postings ask for
- Python
- pandas / NumPy
- scikit-learn
- SQL
- Machine learning
- Statistical modeling
- A/B testing / experimentation
- Feature engineering
- Model deployment / MLOps
- TensorFlow / PyTorch
- Hypothesis testing
- Cloud ML (SageMaker / Vertex AI)
Common mistakes on resumes for this role
- Model names and algorithms listed with no business outcome — "used XGBoost" alone doesn't say what it improved.
- No experimentation vocabulary (A/B test, control group, significance) despite most DS postings expecting it.
- Nothing about deployment or production. A model in a notebook reads differently than a model in production, and postings increasingly ask for the latter.
- Overloaded with academic language (research, thesis, publications) with no translation to product or business impact.
Weak vs. strong
Weak Built a machine learning model to predict churn.
Strong Shipped a gradient-boosted churn model to production, flagging at-risk accounts 3 weeks earlier and lifting save-offer acceptance 22%.