Row 21183

Row ID: 21183 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 21183 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Why?

Not all data scientists work with machine learning, and those who do often engage in limited ML projects.

I'm skeptical that many will implement techniques more advanced than Xgboost.

The role of a data scientist can be ambiguous—many simply write SQL scripts and dabble in Python occasionally. Their notebooks can often be a nightmare to navigate.

If you're excited about GenAI, consider transitioning to a Machine Learning Engineer role. Your DevOps knowledge is highly sought after, and you could even step into an MLOps Engineer position to secure a higher salary.

I previously worked as a senior data scientist before switching to a machine learning engineer role for smarter work and better pay.

If you're interested in GenAI, check out communities like r/llmops and r/LocalLLaMA.

Don't just study data science—it might disappoint you.

Instead, build ML pipelines and LLM apps, and aim for a role in MLE/MLOps.

You'll likely be much more in demand.

EDIT: Typos

FieldValue
text Why? Not all data scientists work with machine learning, and those who do often engage in limited ML projects. I'm skeptical that many will implement techniques more advanced than Xgboost. The role of a data scientist can be ambiguous—many simply write SQL scripts and dabble in Python occasionally. Their notebooks can often be a nightmare to navigate. If you're excited about GenAI, consider transitioning to a Machine Learning Engineer role. Your DevOps knowledge is highly sought after, an…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-21
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Raw Record

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  "text": "Why?\n\nNot all data scientists work with machine learning, and those who do often engage in limited ML projects. \n\nI'm skeptical that many will implement techniques more advanced than Xgboost. \n\nThe role of a data scientist can be ambiguous—many simply write SQL scripts and dabble in Python occasionally. Their notebooks can often be a nightmare to navigate. \n\nIf you're excited about GenAI, consider transitioning to a Machine Learning Engineer role. Your DevOps knowledge is highly sought after, and you could even step into an MLOps Engineer position to secure a higher salary. \n\nI previously worked as a senior data scientist before switching to a machine learning engineer role for smarter work and better pay. \n\nIf you're interested in GenAI, check out communities like r/llmops and r/LocalLLaMA.\n\nDon't just study data science—it might disappoint you.\n\nInstead, build ML pipelines and LLM apps, and aim for a role in MLE/MLOps. \n\nYou'll likely be much more in demand. \n\n  \nEDIT: Typos",
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  "dataType": "comment",
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  "datetime": "2024-05-21",
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