Row 48191

Row ID: 48191 | Dataset Entry | Axioma AXP Content Repository

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If I interviewed you I'd ask questions about it's accuracy, precision, recall, f1-score.

But even then you'd be competing against people that have shown ROI on their models and can demonstrate how to tie their model metrics back to meaningful business KPIs.

If you could successfully answer all that though, you'd probably only be considered for a machine learning intern position.

MLEs are usually roles you grow into because on any given day you could be performing data science, doing data engineering, feature engineering, training machine learning/deep learning/or generative models.

Each one of those tasks are a job on their own.

I recently joined a start up and my job expectations have been pretty insane in terms of breadth, I have done all of the above at some point + implement security protocols/2fa/AWS networking/infrastructure just because we needed our deployment process to be enterprise grade.

These are skills that took many years to acquire, I luckily did web development as a hobby early on and participated in a few ML competitions so many of these things are second nature. On top of that I make sure to keep up with AI news daily, it's honestly a lifestyle and not just a job. But I know tons of guys like me that put the same effort in.

FieldValue
text If I interviewed you I'd ask questions about it's accuracy, precision, recall, f1-score.  But even then you'd be competing against people that have shown ROI on their models and can demonstrate how to tie their model metrics back to meaningful business KPIs. If you could successfully answer all that though, you'd probably only be considered for a machine learning intern position. MLEs are usually roles you grow into because on any given day you could be performing data science, doing data …
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-22
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Raw Record

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  "text": "If I interviewed you I'd ask questions about it's accuracy, precision, recall, f1-score. \n\n\nBut even then you'd be competing against people that have shown ROI on their models and can demonstrate how to tie their model metrics back to meaningful business KPIs.\n\n\nIf you could successfully answer all that though, you'd probably only be considered for a machine learning intern position.\n\n\nMLEs are usually roles you grow into because on any given day you could be performing data science, doing data engineering, feature engineering, training machine learning/deep learning/or generative models.\n\n\nEach one of those tasks are a job on their own.\n\n\nI recently joined a start up and my job expectations have been pretty insane in terms of breadth, I have done all of the above at some point + implement security protocols/2fa/AWS networking/infrastructure just because we needed our deployment process to be enterprise grade.\n\n\nThese are skills that took many years to acquire, I luckily did web development as a hobby early on and participated in a few ML competitions so many of these things are second nature. On top of that I make sure to keep up with AI news daily, it's honestly a lifestyle and not just a job. But I know tons of guys like me that put the same effort in.",
  "label": "r/datascience",
  "dataType": "comment",
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  "datetime": "2024-05-22",
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