What is a Data Science Career Framework?
In summary, it is a is a set of guidelines that clearly defines an employee’s title and expectations of their role. Think of it as a career advancement ladder. It also allows you to see how you can move between roles whilst ensuring that both the company’s and your needs are met.
So, instead of using the very generic title of “data scientist” to address everyone in this field, data professionals would very much appreciate being known for their more specific job titles based on their mid-career specializations. Now, let’s break down each of the 3 section of this framework.
Level 1: Foundation (Entry-Level)
To begin in an entry-level career in data science, you’ll need to be equipped with the Level 1 skills. This includes data wrangling, exploration, modeling, and communication. With these abilities, you’d be able to execute on well-scoped tasks while following the lead of your experienced teammates/team leaders. This is crucial as these Level 1 skills serve as the foundation for each of the specializations in Level 2, and they’re essential regardless of the size of your company or what it does.
Level 2: Application (Mid-Career)
At this stage, experienced data professionals typically specialize in a Level 2 domain depending on your company’s focus and availability. These domains can include Data Engineering, Machine Learning or Advanced Analytics. Therefore, instead of calling everyone in Level 2 a data scientist, we instead call them data engineers, machine learning engineers, and advanced data analysts, to reflect the unique nuances of their scope.
Level 3: Leadership (Senior Role/Management)
For team members seeking for leadership roles (Level 3), they often required to possess a bundle of additional skills — in business, governance, and people — to be a successful data team leader. This is because these roles require generalization and problem-solving across the stack. Thus, you’d more often than not witness successful Level 3s covering more than one Level 2 specialization during their careers.