The author outlines five strategies that will transform your Data Science practice to a Senior role.
You’ve been working as a Data Scientist for a few years, and your goal is to reach the next level. Excelling in your current Data Scientist role is critical, but in many organizations, it alone won’t propel you forward. You’ll need to do more — or differently. In this article, I aim to provide you with valuables ideas and examples that can guide you toward operating as a Senior Data Scientist. Whether you are chasing that internal promotion or considering external applications, I hope this will help you succeed!
Although every organization will have nuances in how they define the different levels of Data Scientists, there tends to be a consensus among many on the fundamental responsibilities and scope of work associated with each level.
In this article, I’ve outlined the differences in scope of work for a 3-tier hierarchy of data scientists. Some organizations have more granular levels, but I believe that the career progression I’ve described holds true in many companies.
Mastering the technical data science skills is a given to become a a Senior Data Scientist. With several years of experience, your technical toolkit should have grown substantially, encompassing a diverse array of data manipulation and preparation techniques as well as a broad repertoire of machine learning models that you can not only apply but also fine-tune, assess for performance, and understand their behavior. Additionally, your years of experience should have also given you a robust understanding of the business you are part of.
But this alone won’t necessarily take you to the Senior/Principal level.
You will need to grow the scope of your work to encompass…