Machine Learning has come a long way from being a method of data analysis for analytical model building to become the strongest progression tool for understanding today’s data-driven world. In a steadily evolving tech landscape, almost every application that is being developed today must be an intelligent application if it wants to be relevant and successful to our modern needs. Chances are you’ve heard about major companies using this machine learning technology, from Tesla and Google’s self driving cars, to amazing breakthroughs in the medical field with machines capable of predicting illnesses, and countless other examples. We don’t need to look very far to see the ways machine learning affects our lives, from grocery shopping to medical check ups, it is all around us!
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Companies like Google and startups like Clarifi have one thing in common: they have identified the highly evolving symbiotic relationship between their need and importance of exploiting the power of systematic machine learning approach. For example, Google’s Translate and Photos along with Voice Search is a result of applying machine learning on existing domains. Clarifi has used machine learning to develop software and understand videos, which can add value to their company as an advertisement tool. This relationship has naturally altered the hiring process for ML experts. Companies today face immense competition (especially in the Silicon Valley) to find and attract new talent in the Machine Learning and Data Sciences domains to be ahead of competition. This in turn has impacted factors that Hiring Managers need to consider while scouting for, interviewing and having on board their most-poached category of employees, the Data Scientists. Here are the big three factors that recruiters and hiring managers would have to deeply ruminate about when it comes to Machine Learning openings.
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