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by N_trglctc_joe
2628 days ago
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Question related to this: I'm applying to jobs in data science (straight outta grad school), and I'm not really sure how to market myself. The problem I keep running up against is that data science is such a broad term that it's hard for me to express how I can provide value to a company without speaking in empty generalities. From my reading of job postings, it seems as though what's called "data science" at one company is "software engineer" at another and "machine learning developer" at a third. How do working data scientists view their role within a company, especially how do their differentiate their purpose from the tools they use? |
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The first is Statistics <-> Machine Learning. On one hand, you can be a data scientist that primarily uses statistics to model user behavior, create metrics, test hypotheses that then inform product design. On the other hand, you can prototype and develop machine learning systems (recommendation engines, predictive analytics etc).
The second axis is developing production code. Some data scientists live in their notebooks and analyses and never write code that directly makes it into production. Others are expected to sit alongside the SWEs and write production level code for the models they have created.
Examples: Data Scientists at Google are statistics heavy and not often writing production code (though this varies by team). On the other hand Quora Data Scientists are mostly developing machine learning models and are expected to write production level code to implement these.