Data Science resumes must bridge the gap between scientific experimentation and production delivery: - State Exact Metrics: F1-score, AUC-ROC, latency, BLEU score, or perplexity. - Emphasize Deployment: Show how models were served (FastAPI, ONNX, Triton, Docker, AWS SageMaker) rather than just trained on Jupyter notebooks. - Dataset Scale: Mention dataset size (e.g., "Trained on 2.4M tokenized text records").
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Yes! Kaggle Expert or Master titles, or top 5% finishes in featured competitions, serve as strong proof-of-work signals for recruiters.
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Avoid toy MNIST models. Build end-to-end applications: Retrieval-Augmented Generation (RAG) engines, multimodal search, and ONNX edge inference.
Math fundamentals (linear algebra, calculus), PyTorch, ML algorithms, MLOps tooling, and serving production models at scale.
Master bias-variance trade-offs, regularization techniques (L1/L2), precision vs recall, gradient descent variants, and transformer architectures.