> The most valuable people in the data chain will be those that can take idea to near-production. Having hired many data scientists/ML engineers over the years

> The most valuable people in the data chain will be those that can take idea to near-production. Having hired many data scientists/ML engineers over the years, people that build robust automated intelligence directly into products are extremely rare. I've estimated a maximum of 10K people in the entire world. They also command the highest salaries, not coincidentally. Very few people have both the statistics and engineering backgrounds as well as temperament to be successful, particularly when the problem requires new data sources or new types of models. There are some real simple practical hurdles such as the need to implement robust tracking that allows data snapshotting at the time when a decision needs to be made without affecting product performance, as well as figuring out how to gather data on users/situations that are actually important for moving the needle (first time users, casual users, etc.) There is also a mismatch between the best frameworks for prototyping/research and implementation which (at least at the companies I've worked for) can be summarized as "Java is good for application development, not ML. Python is good for ML, not application development."