Six Tips on Building a Data Science Team at a Small Company

When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you’re the data scientist hired to make this happen, we have some tips to help you face the task head-on.

When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you’re the data scientist hired to make this happen, we have some tips to help you face the task head-on.

Being the only data scientist at a company is tricky. You may be expected to be the expert at everything data or code related. A good starting point is to break down the most important deliverables in the company. Understanding these deliverables and deconstructing them until you can lay out the most important data sources and processing steps is important in understanding exactly what needs to get done for this company.

Staying organized is one of the most important aspects of building a successful team, but you don’t have to reinvent the wheel. There are many project planning practices that can help provide structure to your data processes. For example, The Data Science Hierarchy of Needs is a great resource for staying on track and organized during this planning process.

It would be nice to deliver AI solutions for your company right away, but there are many foundations that need to be set in place before that can realistically happen. The Data Science Hierarchy of Needs, and other project planning tools like it, can help you structure a sound, sustainable path to your company’s data science goals.

As the first data scientist, you can realistically expect that your non-technical colleagues will not understand your work and all the effort that goes into it. Therefore, it will be on you to report wins along the way towards deploying your first data model. This will ensure that your company stays up to date with your progress, and build trust in your ability to build and deliver.

For example, a reliable data flow will be the cornerstone of the productivity of any data team. It’s a foundational part of the pyramid and it will empower you to swiftly tackle a variety of problems.