From the course: Data Science Team Lifecycle Management

How to determine what level of data scientist you need and how many

From the course: Data Science Team Lifecycle Management

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How to determine what level of data scientist you need and how many

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- Sometimes it can be difficult to understand what level of data scientist you might need to hire. Let's discuss the various levels and see what sets them apart. The first thing to understand is that job levels and job titles are often determined based on salary. A junior data scientist, for example, is paid less than a data scientist. A data scientist is paid less than a senior data scientist, and so on. However, there is no uniform hierarchy of data scientists across all companies. So instead, we'll discuss the three primary groups, data scientist, senior data scientist, and principle data scientist. An entry-level data scientist contributes to the overall business, but usually works within a larger team and is not charged with leading projects. An entry-level data scientist should have a command of the three primary skill sets we covered in the previous video. Those skills mapped to the three main areas of math and statistics, coding, and machine learning theory. Senior data scientists typically have at least two to three years of experience and have two defining characteristics. The first is that they should have a firm grasp on what it takes to see a machine learning model through to production. This is quite possibly their most defining characteristic, and I would never bestow the title of senior data scientist upon anyone who doesn't have this knowledge or experience. But that's not the only defining characteristic, the other's leadership. A senior data scientist should start exhibiting leadership qualities and mentoring skills for the junior people on the team. These leadership skills don't always come naturally, which we'll discuss in an upcoming video. The third level is that of the principle data scientist. This title is reserved for the most senior people and typically represents deep expertise in a specific business or technical area. Principles can work independently and handle deep complex aspects of the project autonomously. Their deep knowledge also qualifies them to scope projects under valuable contributors during problem framing. Principle data scientists typically have, at minimum, five years of experience, but that's not a hard and fast rule and there can always be exceptions. Irrespective of which level of data scientists you need, remember the simple rule, the higher the level of data scientist, the greater their contribution to the business. It's that simple. Also, there are very different salary expectations associated with each of these three levels, so don't expect to recruit a principal data scientist with a compensation package equivalent to an entry-level data scientist. Lastly, remember that the data scientist hierarchy doesn't necessarily mean the higher up the person, the more management responsibilities they take on. Many data scientists prefer to be individual contributors, which we'll discuss in an upcoming video. And there you go. Now you know the three primary levels of data scientists and what separates them.

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