6 ms·

Are there deep learning roles that focus more on software engineering and using the tools rather than having a deep understanding of statistics?

throwaway6734 5y ago
I think they're called research engineering roles or ML engineering
jstx1 5y ago
There are. But 1) the titles will vary a lot (software engineer, ML engineer, research engineer, data scientist etc.) which makes it hard to locate those jobs and to move in the job market in general 2) you still need a reasonable amount of theory (not necessarily too much statistics) to use the tools well. And in all likelihood you will be tested on it in some way during the interviews. 3) the interviews/job descriptions that don't emphasise the theory often will be for jobs where you get a title like Machine Learning Engineer but you focus more on the infrastructure rather than on the ML code
time_to_smile 5y ago
> having a deep understanding of statistics? As someone with a strong background in statistics, please tell me where I can find DS jobs that require this. For me and all my statistics friends in DS we find much more frustration in how hard it is to pass DS interviews when you understand problems deeper than "use XGBoost". I have found that very few data scientists really even understand basic statistics, I failed an interview once because an interviewer did not believe that logistic regression could be used to solve statistical inference questions (when it and more generally the GLM is the workhorse of statistical work). And to answer your question, whenever I'm in a hiring manager position I very strongly value strong software engineering skills. DS teams made up of people that are closer to curious engineers tend to greatly outperform teams made up of researchers that don't know you can write code outside of a notebook.
- disgruntledphd2 5y ago
  A good conceptual understanding of statistics is always helpful. It's not really tested for in most places though, where they regard a DS as a service that produces models.
fault1 5y ago
I would say on average MLE roles tend to be more SWEng heavy. But some roles are as much creating infrastructure as running the tools.

throwaway6734 5y ago

I think they're called research engineering roles or ML engineering

jstx1 5y ago

There are. But 1) the titles will vary a lot (software engineer, ML engineer, research engineer, data scientist etc.) which makes it hard to locate those jobs and to move in the job market in general 2) you still need a reasonable amount of theory (not necessarily too much statistics) to use the tools well. And in all likelihood you will be tested on it in some way during the interviews. 3) the interviews/job descriptions that don't emphasise the theory often will be for jobs where you get a title like Machine Learning Engineer but you focus more on the infrastructure rather than on the ML code

time_to_smile 5y ago

> having a deep understanding of statistics? As someone with a strong background in statistics, please tell me where I can find DS jobs that require this. For me and all my statistics friends in DS we find much more frustration in how hard it is to pass DS interviews when you understand problems deeper than "use XGBoost". I have found that very few data scientists really even understand basic statistics, I failed an interview once because an interviewer did not believe that logistic regression could be used to solve statistical inference questions (when it and more generally the GLM is the workhorse of statistical work). And to answer your question, whenever I'm in a hiring manager position I very strongly value strong software engineering skills. DS teams made up of people that are closer to curious engineers tend to greatly outperform teams made up of researchers that don't know you can write code outside of a notebook.

disgruntledphd2 5y ago
A good conceptual understanding of statistics is always helpful. It's not really tested for in most places though, where they regard a DS as a service that produces models.

fault1 5y ago

I would say on average MLE roles tend to be more SWEng heavy. But some roles are as much creating infrastructure as running the tools.