Wrong but useful. Working @Twitter, tweeting about data science and people management.
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"Always has been"
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This could be a shorter term concern, such as bringing in someone with relevant subject matter expertise but few data science-specific skills.
Or it could be a bigger multi-year strategy, such as growing a new generation of data scientists to step up when some of your current senior folks inevitably seek other opportunities.
You can’t make anyone into anything--sometimes a junior will just not have the will or the skill to get to a certain endpoint--but the surest way to grow them into an effective data scientist is to have them work on the problems your organization actually needs to solve.
Even if the junior says they aren’t interested in growing the direction you’re nudging them, these types of projects will help them understand what moves the needle for their stakeholders.
This builds their ability to identify what work is actually useful and it is, in my opinion, one of the most important experiences a junior data scientist can have.
If there’s any remedy to the using-neural-nets-instead-of-logistic-regression industrial complex, it’s giving your junior the opportunity to feel appreciated by the customers of their work.
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