Difference between data scientist and machine learning engineer?
In the San Francisco / Bay Area, what is the difference between a machine learning engineer and a data scientist? More specifically, who is more likely to be responsible for prototyping and building machine learning models and performing feature engineering / analysis?
In the past and for tabular datasets (predicting bankruptcy, fraud, heart disease, marketing responses, etc), I would have said that the data scientist would be in charge of creating machine learning models and the machine learning engineer would be in charge of deployment. I’ve had a lot of interviews recently, and in San Francisco in particular it seems that most startups have taken the responsibility of predictive / ML models out of the data scientist role and given it entirely to the ML engineer. In many places it seems the data scientist role has become solely about A/B testing and analysis. Is this part of a broader trend or are my 10 or so personal anecdotes not representative of the industry at large?
In the past and for tabular datasets (predicting bankruptcy, fraud, heart disease, marketing responses, etc), I would have said that the data scientist would be in charge of creating machine learning models and the machine learning engineer would be in charge of deployment. I’ve had a lot of interviews recently, and in San Francisco in particular it seems that most startups have taken the responsibility of predictive / ML models out of the data scientist role and given it entirely to the ML engineer. In many places it seems the data scientist role has become solely about A/B testing and analysis. Is this part of a broader trend or are my 10 or so personal anecdotes not representative of the industry at large?