Thanks K for this article! I'm really glad it came out while I was spending my Sunday watching videos to understand what an MLE actually does, haha. It would be fantastic if you could share more articles like this, explaining various data job families you worked in/with, what they do, and their skill set. Data is a fascinating field, but there are clearly many roles out there
Very interesting. At some point, I was stuck between deciding in investing more on the data science side of things or the MLE side of things when preparing for data roles, only to discover that MLE are fundamentally software engineers. Boris meinardus made it very clear when I talked to him. So before diving into such a role, one needs to love coding more than delivering insights. Add to that what you just said about ML not always delivering immediate results compared to data science. This clears things up a little bit more for me
Thank you Mouad for sharing your experience. I have to correct you on one thing though, MLEs are not software engineers. They’re ML specialists if you’d like. I can be an MLE but I know for a fact I could never be a SWE with my current set of skills. MLEs oversee ML projects from data collection/engineering to model optimisation and deployment and finally making sure it’s scalable. I got that clarified this week during my meeting with the Spotify MLE I spoke to. But you are right when you say that having an interest in coding is important to be an MLE since a lot of the time they code with languages beyond Python and SQL, like Scala for instance. I hope this clears things up even more!
Actually it all depends on the companies like always, that’s why I specified “Tech data scientist” in the title (I edited that after I emailed the post, I realised I forgot to specify). But yes, in most tech companies, the ML is done by researchers or MLEs (who are also responsible for deploying and scaling the model), the data scientists also do research but one that focuses on bringing immediate value, and when ML is used, it’s a tool to answer business problems and only if there are real incentives to do it (and time!)
Thanks K for this article! I'm really glad it came out while I was spending my Sunday watching videos to understand what an MLE actually does, haha. It would be fantastic if you could share more articles like this, explaining various data job families you worked in/with, what they do, and their skill set. Data is a fascinating field, but there are clearly many roles out there
Good timing then!! I definitely will!
Very interesting. At some point, I was stuck between deciding in investing more on the data science side of things or the MLE side of things when preparing for data roles, only to discover that MLE are fundamentally software engineers. Boris meinardus made it very clear when I talked to him. So before diving into such a role, one needs to love coding more than delivering insights. Add to that what you just said about ML not always delivering immediate results compared to data science. This clears things up a little bit more for me
Thank you Mouad for sharing your experience. I have to correct you on one thing though, MLEs are not software engineers. They’re ML specialists if you’d like. I can be an MLE but I know for a fact I could never be a SWE with my current set of skills. MLEs oversee ML projects from data collection/engineering to model optimisation and deployment and finally making sure it’s scalable. I got that clarified this week during my meeting with the Spotify MLE I spoke to. But you are right when you say that having an interest in coding is important to be an MLE since a lot of the time they code with languages beyond Python and SQL, like Scala for instance. I hope this clears things up even more!
Interesting thank you!
I thought that MLEs were supposed to upload/deploy the models first built by data scientists and making them more efficient.
Actually it all depends on the companies like always, that’s why I specified “Tech data scientist” in the title (I edited that after I emailed the post, I realised I forgot to specify). But yes, in most tech companies, the ML is done by researchers or MLEs (who are also responsible for deploying and scaling the model), the data scientists also do research but one that focuses on bringing immediate value, and when ML is used, it’s a tool to answer business problems and only if there are real incentives to do it (and time!)