Data science career progression and ownership

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Are you wondering how to measure career progression in data science? Or maybe you want to learn how to progress your data science career to the next level? Well either way, you are in the right place.

In this article we discuss data science career progression and how career progression relates to your scope of ownership. First, we define exactly what we mean when we talk about ownership. After that, we provide more context on why scope ownership is a good measurement that can be used to measure data science career progression across many different industries and specialties. Finally, we describe different levels of ownership that individuals may have as they progress through their careers.

What does ownership look like in data science?

What exactly are we talking about when we describe ownership in data science? In this section, we will clearly define what we mean by ownership. When we describe a data scientist as having ownership over a given area, we mean that the data scientist is the single person who is accountable for all outcomes in that area. This does not mean that person will personally complete all of the work that is required to achieve those outcomes, but rather that they will be responsible for ensuring that the right work is completed by the right people in order to achieve the outcomes they are looking for.

In the beginning of your career when you have a smaller scope of ownership, many of the areas you own will not have enough work to require more than one person to contribute to them. That means that you will likely be the one who is doing the hands-on work required to complete most of the work in your area of ownership. As you progress through your career and your area of ownership grows, there will be too much work that needs to happen in your area of ownership for one person to complete. At this point, you will need to begin delegating work to other members of your team.

Why is ownership a good measurement for career progression?

Why is scope of ownership a good tool for measuring data science career progression? Scope of ownership is a good tool for measuring data science career progression because it is a general concept that is applicable across many different industries and specialties within the field of data science. That means that there is no need to develop a specific framework for every different career path and industry.

The term data science is broad and there are many different types of work that might be completed by data science teams. Some data science teams work primarily on reporting and data aggregation, whereas others build machine learning models that are incorporated into user facing products. The specific skills and competencies that are required for different types of data science roles can vary a lot from one role to another. That being said, all of these different roles still have a concept of ownership and different levels of ownership that can be achieved.

In addition to being broadly applicable across data science teams that work on multiple different types of work, ownership is also an appropriate measurement of career progression for people on both the technical career tract and the people leadership career track. Individuals who are on the people leadership career track are often accountable for the business outcomes that are achieved in a particular area of ownership. Individuals who are on the technical track are often responsible for technical outcomes within their area of ownership.

Areas of ownership in data science

In the following section, we will provide examples of different areas of ownership that a data scientist might have at different points in their career. We will start out by talking about areas of ownership that are appropriate for junior data scientists who are just starting their career. As we progress, each area of ownership will get larger and be more appropriate for someone who has progressed further into their career.

Ownership of data science tasks

The first area of ownership is ownership over a certain task or a selection of tasks. Sometimes, these tasks will be standalone tasks such as maintenance tasks or ad hoc reporting tasks. Other times, these tasks will represent a portion of the work that needs to be completed for a larger project that is owned by another team member. This scope of ownership is most appropriate for junior data scientists who are near the beginning of their career.

Ownership of small data science projects

The next area of ownership that a data scientist might have is ownership over a small project. When we say a small project, we are speaking specifically about a project that can be completed by one person rather than a project that requires the work of multiple data scientists. At this level, the data scientist who owns the project will not have to break work up and delegate it to other data scientists. This scope of work is generally most appropriate for junior data scientists to mid level data scientists, depending on the complexity and visibility of the project.

Ownership of large data science projects

The next area of ownership that a data scientist might have is ownership over a large project. When we say a large project, we are speaking specifically about a project that cannot be completed by a single data scientist. At this level, the data scientist who has ownership over the project will start to break up the work that needs to be completed into tasks that can be delegated to other data scientists. This scope of work is most appropriate for mid level data scientists to senior data scientists, depending on the complexity and visibility of the project.

Ownership of a data science domain

The next area of ownership that a data scientist might have is ownership over a domain or an application area that the team contributes to. In many cases, there will be multiple different projects happening in a given domain area at a given time. These individual projects might be owned by different data science team members, but ultimately the person who has ownership over the domain is accountable for making sure that all of the projects in that area are on track. This scope of ownership is appropriate for senior data scientists to staff data scientists depending on the size of the domain and the number of projects that are going on in that domain.

Ownership of a data science team

The next area of ownership that a data scientist can have is ownership over all of the work that is completed by a particular data science team. This should encompass all of the projects and tasks that are completed by the team across multiple domain areas. For people who are on the technical track, this scope of ownership is generally most appropriate for a senior data scientist or staff data scientist depending on the size and scope of the team. For people who are on the people management track, this scope of work is generally most appropriate for a data science manager.

Ownership of multiple data science teams

The next area of ownership that a data scientist can have ownership over is all of the work that is completed by multiple teams. This should encompass all tasks and projects that are completed by any of the teams in question. For people who are on the technical track, this scope of ownership is generally most appropriate for staff data scientists or senior staff data scientists depending on the size and scope of the teams. For data scientists who are on the people management track, this area of ownership is most appropriate for senior managers.

Ownership of all data science teams

The final area of ownership that a data scientist can have is ownership over all data science activities that happen at a company. This encompasses all projects and all tasks that are completed by data science teams. For people who are on the technical track, this scope of ownership is generally most appropriate for principal data scientists. For people who are on the people management track, this scope of ownership is generally most appropriate for directors.

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