This is probably the best post I've seen on this topic. Here's my favorite bit:
A common trap I see are people who come out of Data Science programs joining these positions expecting to be using sexy things like Spark and applying RNNs to their work. But sadly, they want to live on top of a mountain of foundation work that needs to be done first, both from an engineering standpoint and from cultural standpoint. The mismatch is brutal.
Being the first person specifically hired to handle data, it’s very unlikely any pieces of the pyramids are sturdy. It’s a multi-year, cross-functional, full company effort to get all the pieces in place. Nurturing those all those pieces in parallel is a big part of the job.
"I'm a data scientist, it's not my job to handle [infrastructure / reporting / etc.]." If you're early at a company, regardless of your title, your job is to do whatever is needed to help the company grow using data. What most folks miss is that it's actually an incredible opportunity to understand and/or have built of the core foundations that data science relies upon at your org. This path will frequently be a long one, but it can lead to your having an outsize impact on the company, and in turn on your career.Read more...