Row 98509

Row ID: 98509 | Dataset Entry | Axioma AXP Content Repository

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As an MBA who understands business, picked up the technical skills, and has now been working as first an IC then manager and now director over data science for more than a decade, the truth is that for most DS roles the soft skills and business acumen are more critical than the technical skills. Unless you’re working in a FAANG company and you’re developing applications straight off of research papers mostly what you need to be able to do is understand the business problem, understand and manage the data, select the right model, and use an off the shelf implemention combined with good SWE practices to deliver an effective solution. None of that actually requires getting down into the math of, for example, how the machine actually minimized the loss function of your XGboost model (or even what the loss function is for that matter). SWEs use libraries all the time without understanding the low level details of their construction, it’s not a big deal.

Now if you’re doing a lot of more stats type stuff like designing full factorial experiments then yes, you need to understand the underlying math. But I’d guess that for most data scientist in non-big-tech firms most of their work is just building a succession of classification and regression models that are fairly plug and play.

FieldValue
text As an MBA who understands business, picked up the technical skills, and has now been working as first an IC then manager and now director over data science for more than a decade, the truth is that for most DS roles the soft skills and business acumen are more critical than the technical skills. Unless you’re working in a FAANG company and you’re developing applications straight off of research papers mostly what you need to be able to do is understand the business problem, understand and manage…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-25
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Raw Record

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  "text": "As an MBA who understands business, picked up the technical skills, and has now been working as first an IC then manager and now director over data science for more than a decade, the truth is that for most DS roles the soft skills and business acumen are more critical than the technical skills. Unless you’re working in a FAANG company and you’re developing applications straight off of research papers mostly what you need to be able to do is understand the business problem, understand and manage the data, select the right model, and use an off the shelf implemention combined with good SWE practices to deliver an effective solution. None of that actually requires getting down into the math of, for example, how the machine actually minimized the loss function of your XGboost model (or even what the loss function is for that matter). SWEs use libraries all the time without understanding the low level details of their construction, it’s not a big deal. \n\nNow if you’re doing a lot of more stats type stuff like designing full factorial experiments then yes, you need to understand the underlying math. But I’d guess that for most data scientist in non-big-tech firms most of their work is just building a succession of classification and regression models that are fairly plug and play.",
  "label": "r/datascience",
  "dataType": "comment",
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  "datetime": "2024-05-25",
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