Over the years since Data Science has emerged as a discipline, the focus has shifted more toward model deployment, sentiments echoed by May here. This demand, in turn, has made Data Scientists more valuable when it comes to software engineering and machine learning skills.
As a result, skill sets of software engineers and data scientists are slowly overlapping in terms of product-facing data science applications. Data scientists who are more adept at model building are being trained for product deployment.
In the past, the industry landscape didn’t always look like this. Data science and software engineering were almost contrasting disciplines. When business leaders found the potential, integration started to take place, and data scientists are now expanding their skills to cover software.
What does this mean for established and aspiring data scientists?
Both startups and huge companies consider different factors for hiring talent. Startups are more willing to take chances on new data scientists. Bigger-scaled organizations tend to favor people who will stick around for a more stable role.
If you’re a data scientist, the best way to embrace this shift is to look at data science as a product development function. Always ask, “How accurate does my model have to be?” The answer to that is as accurate as needed to make UX as flawless and smooth as possible. No more, no less!
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