Chaining has its own benefits. But I think this doesn't fit the definition of "Pythonic". Again, "Pythonic" is highly debatable. But, You can always break down big chain of operations, into smaller chain using good variable naming in-between.
Many operations are implemented as iterator in python on list, like filter, groupby.
Looking at your code, its looks like you're not doing lazy computation. (Correct me if I wrong). This could be huge performance impact, depending upon use case of list.
I've never worked at a FAANG, too.
But main reason I want to work at one is that ability to do project at a scale which is not possible anywhere. Few project are no use for small companies/startups.
For example: Optimizing compile time (no need to invest for extra 1 minute speed up), working on high quality labelled data (i came from ML background, this is not possible in most of startups), analytics on data (questionable ethically), working on Ad platforms, working on large scale system.
In the last, Imagine, even making simple changes have bigger impact on real world.
Remote: Yes
Willing to relocate: Probably yes
Technologies: Python, Flask, PyTorch, Spacy, C
Areas: NLP, CV, Optimization
Résumé/CV: https://read.cv/dipkumar/
Email: [email protected]
Why hire Dip: - Worked as machine learning engineer + research engineer + backend engineer.
- single handedly deployed multiple ML system in production
- I believe creating baseline first and improving from it instead of going with biggest weapon.
- fast learner (worked on various project ranging from stitching photos to speech intent detection to solving NP-hard problems)
Why not to hire Dip:
- Need Research Scientist instead of MLE or Research Engineer
- Need senior (experienced) backend engineer