Row 97447

Row ID: 97447 | Dataset Entry | Axioma AXP Content Repository

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My company splits the data scientists into scientists and ML Engineers. The scientists they want to build robust models with optimal features, parameters, model types, etc. There is a lot of experimentation there. Once it is done, it goes to the ML Engineer to kick it into production. Smaller Data Science operations require data scientists to have more of a mix of skills, but at scale, they can specialize.

Personally, I believe my company overvalues the scientific portion of data science. For complex use cases, yes, but for most use cases, an out of the box existing solution with common sense features works. My manager was criticizing Master’s degree level data scientists because they are overtrained on implementing existing ML algorithms on different data and use cases. I think that approach works for at least 80% of use cases. Rather, he pretty much only hires Ph.D candidates with intense academic research backgrounds. But half of them hate coding 🤷‍♂️

FieldValue
text My company splits the data scientists into scientists and ML Engineers. The scientists they want to build robust models with optimal features, parameters, model types, etc. There is a lot of experimentation there. Once it is done, it goes to the ML Engineer to kick it into production. Smaller Data Science operations require data scientists to have more of a mix of skills, but at scale, they can specialize. Personally, I believe my company overvalues the scientific portion of data science. For …
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-25
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{
  "text": "My company splits the data scientists into scientists and ML Engineers. The scientists they want to build robust models with optimal features, parameters, model types, etc. There is a lot of experimentation there. Once it is done, it goes to the ML Engineer to kick it into production. Smaller Data Science operations require data scientists to have more of a mix of skills, but at scale, they can specialize. \n\nPersonally, I believe my company overvalues the scientific portion of data science. For complex use cases, yes, but for most use cases, an out of the box existing solution with common sense features works. My manager was criticizing Master’s degree level data scientists because they are overtrained on implementing existing ML algorithms on different data and use cases. I think that approach works for at least 80% of use cases. Rather, he pretty much only hires Ph.D candidates with intense academic research backgrounds. But half of them hate coding 🤷‍♂️",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak12VWlnVWxVY0NaZkxMY3ZLU3JfT19aLTI4azNTNC14UVpmWVNQd1FaUmhBQUp3WHRnY2ZMWEhpZHlQQmoyaFc3MHdnemFCRDE5Y0R4NFdabmRRU0M3R0E9PQ==",
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}
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