Castro also puts a premium on problem-solving skills. "You and your team will typically be set a high level objective for which you need to determine the best cause of action. Unlike other areas, there is no one recipe for data science. Often you are not trying to find the right answer to a question, you're trying to find the right questions to ask in the first place."
If you speak to enough data scientists one thing you will hear is how much time they spend cleaning up data rather than analysing it. Sandra Greiss, a data scientist at online retailer Asos, says that even though this takes up eighty percent of her time, and the availability of tools for data cleansing (Trifacta, OpenRefine, DataWrangler), she would only ever want to do it by hand.
"It is frustrating but I think it is also a relief when you are done with it as you will be using something which is correct," she says. "I don't think you would want to rely on a tool. You have to see it yourself."
One skill that is of growing importance in data science circles, and within the enterprise, is machine learning.
"Machine learning is a no-brainer to me. That is the true heart of data science," says Mike Ferguson, an analyst at Intelligent Business Strategies.
"People want to have a pattern detection and a view into the future, so the traditional career in reporting is no longer enough, which is a key reason machine learning is critical. The days of taking data out of a database and doing the analysis somewhere else is done, the data is too big."
Asos' Greiss has seen it grow in importance within the industry: "I think it was already important, and I was asked about it at interview, but I don't have machine learning skills. I think machine learning is something you can pick up pretty quickly if you want to. Now it is something they will probably ask for because it has expanded so much and there is so much free online material available to give you some idea about how to do machine learning."
Castro at Expedia says that a great data scientist must be "persistent, highly energetic and motivated". His advice for any prospective candidates is to: "Follow lots of other data scientists on social media - Twitter - read blogs, learn a new data science technology, practice on Kaggle or possibly enrol in an intensive data science course.
"After you've done that, try to get a data science internship. Make sure that you'll be working on a cool end-to-end data science project or a deep dive on a specific piece with a measurable output, e.g. a new algorithm that you can A/B test, rather than just doing what everyone else doesn't want to do, e.g. unit tests, though you will still learn."
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