Home > Technology > How Junior & Senior Data Scientists Differ From Each Other

How Junior & Senior Data Scientists Differ From Each Other

Junior & Senior Data Scientists Differ From Each Other

While choosing among the varied job positions advertised on the job portals, you are asked to select the seniority level to apply to. There exist three levels under seniority – the junior, mid-level, and senior. So, how to go about selecting the job level? 

It depends on the following: To what length you can leave someone alone to work on a task while not checking in at all. Instead of choosing the job seniority level, basis the skillset, level of education, years of experience, subject expertise, knowledge of toolsets, maturity level, or something else, the aptest way would be thinking holistically.

You must consider your capabilities of doing a data science project based on how much assistance you would need for its completion. That’s how you define your seniority level before applying for a data science job. 

Example Scenario to Check on to Your Job Seniority Level

Suppose, your company asks you to build a predictive model on a given subject of interest, and it is required to be submitted in six months. This sophisticated task involves combining internal and external data sets, fact-finding, integrate big data sets, communicating with other divisions of the company, involvement of the firm’s biggest client, statistical & mathematical modeling, and everything needs to work as expected. 

In the above-presented example scenario, we will assign the senior, junior, and mid-level data scientist job roles, as provided below: 

Senior Data Scientist: You give the project to him and he will take care of each aspect of the project. One doesn’t need to check into that.

Mid-Level Data Scientist: You will need to check-in, at least, on a monthly or weekly basis. Also, you will need to assign a senior data scientist to oversee his work. 

Junior Data Scientist: One would need to check on his work on a day-to-day basis. Both, mid-level, and senior data scientist would need to oversee his work. 

How to Set the Role for Yourself?

Provided the above-mentioned talk on choosing your role in terms of seniority among the data science job positions, it has been made quite clear that eventually, everything will matter. All aspects of your educational and professional backgrounds will come into play, ranging from years of experience, maturity, familiarity with toolsets, data science skillset, to your education level.

Each one of the above-presented aspects will somewhere or the other, affect the aforementioned six-months project. And hence, think holistically while choosing the seniority level in the jobs you apply to. See for yourself where you fit in the job description provided. To dissect the advertised job description, and determine, what seniority level it is asking for, or in other words, demanding. 

Ability to Get Things Done is the Biggest Differentiator Among the Three

The most apparent differentiation among the three seniority levels in the data science job roles is the acquired ability to get things done, or in other words, the capability of completing the project with the desired outcome. A junior data scientist may be able to handle certain aspects of the project, but will eventually need the help of a senior person at his firm to guide him through the completion of the concerned task.

A mid-level data scientist would be better equipped in dealing with the complexities of the project but would seek guidance at some point in the context of the entirety of the project. However, when you will assign the same project to a senior data analyst, or scientist, he will get things done on his own. And, that’s what makes him the most senior in the hierarchy.



TAGS , ,