A lot of my articles, as well as much of the writing on data science in general, focus on the work of individual data scientists. In this article, though, I want to focus on something different: the data science team. But first, let’s define what such a team usually consists of. Although this configuration isn’t set in stone, here is an example of a data science team: a few data scientists, a data engineer, a business/data analyst and a data science manager.
The specific composition of the team is less important than how the team works together, however. With that being said, let’s look at the tools and methods you can use to improve collaboration among your data science team, whether you are a data scientist, a manager or possibly a technical recruiter.
3 Ways to Become a Better Data Science Team
- Planning and grooming.
- Stakeholder updates.
- Retrospectives.
Planning and Grooming
This first tool is a combination of planning and grooming. These terms can be a little muddled, though, so let’s define them first.
Grooming falls under the umbrella of organization, but what sets this process apart from planning (in certain companies) is that it serves as the first review of whatever is in your backlog. This queue may be composed of several Jira tickets or other general tasks that your team has come up with over time but has not yet prioritized into an active process.
You can think of planning as more specific on a sprint level. Even if you don’t use Jira, you can still plan weekly, bi-weekly, or on whatever cadence you prefer, and log it with more check-ins. Typically, in these check-ins, you’ll discuss upcoming projects. More importantly, though, you’ll address the digestible tasks of a particular project for that given week or time period.
Here are a few takeaways and benefits that can come from collaborating on planning and grooming:
- Assigning the level of effort for a particular task.
- Assigning importance or priority.
- Avoiding redundancies.
- Highlighting for yourself what you’re focusing on for the week.
- Discovering whether anyone else has worked on something similar and can help you or make the task more efficient.
Once again, …….
Source: https://builtin.com/data-science/how-to-become-better-data-science-team