- Metadata:
- #article #data-science #career
- The difference between a scientist and engineer is prototyping vs productionizing
- Data scientist - use statistical and ml techniques to build models on top of data
- Data engineer - use software and processing tools to build pipelines for data
- ML scientist - use state-of-the-art ML models to prototype ML models
- ML engineer - use high-level ML framework to train and productionize ML models
- There are roughly twice as many open engineering roles vs scientist roles
- With frameworks like Tensorflow and PyTorch, setting up the prototype is getting easier but the “boring” skills like building an ETL pipeline is getting increasingly scarce