Human resources perspective
I I was talking to my manager over coffee the other day, and the conversation turned to the topic of artificial intelligence (AI). In relation to that topic, he commented, “In the field of AI, even if many people talk, it doesn't get across.'' He's mostly right, but it feels like the success or failure of an AI project is determined from the beginning. Recruitment points.
Over the past few months, I've been interviewing with a variety of companies in a variety of industries for data analyst, junior data scientist, or AI engineer positions. During the interviews, I noticed many differences between these companies I interviewed with. They can be classified into two categories: AI leader And that AI laggards.
“'' also exists.The rich get richer and the poor get poorer.” phenomenon occurs, and the reason is simple.
Before we get into it, we need to divide individuals into two groups.
amateur
This group of people are individuals with a keen interest in data science who are 1) in the process of changing jobs, 2) have just changed jobs, or 3) have up to two years of work experience.
This group of people wants to get their hands dirty. The dirtier the better. They focus on improving their technical skills as quickly as possible. This includes 1) the ability to work with like-minded people, 2) the opportunity to learn on the job every day, and 3) exposure to production-grade code and end-to-end ML pipelines. Masu.
professional
People in this group have years of experience working in AI. They know how to build and deploy ML models, plan and execute ML projects on specific timelines, and know the ins and outs of building a successful data science team.
At this stage in their career, these people are making a lot of money. They are very popular and…
