See What Recruiters See

Here's the fix: Explicitly name TensorFlow, PyTorch, Keras, scikit-learn, XGBoost

Here's the fix: Show metrics: "Achieved 94% accuracy, reduced fraud by 37%"

Here's the fix: Create dedicated skills section for Python, R, SQL, cloud platforms

Here's the fix: Translate models to business outcomes: "$2.3M revenue uplift from recommendation engine"

Here's the fix: Describe each ML project with problem, approach, tech stack, results

Name TensorFlow, PyTorch, Keras, scikit-learn, XGBoost, Spark MLlib

Include accuracy, precision/recall, F1, AUC, RMSE metrics

Translate technical results to business metrics: revenue, cost savings, efficiency

Structure projects as: Problem → Approach → Tech Stack → Results → Impact