“As my team started growing, I realized that diversity comes in so many dimensions.”
The diversity comes from the fact that many of her team members are not native English speakers and have different business and academic backgrounds.
Within the team, they defined 4 machine learning roles so that it is clear who is responsible for what. These include machine expertise, machine learning engineers, MLOps Engineers, and product manager.
In the interview, it becomes clear that great benefits of a diverse data team at Carl Zeiss AG are the different, interesting perspectives and new approaches to problem solving.
This thesis is also supported by the World Economic Forum, which has already published an article on the topic of diversity benefits in AI and data departments in 2019. “Non-homogeneous teams are more capable than homogenous teams of recognizing their biases and solving issues when interpreting data, testing solutions or making decisions.”
Diversity in Data Teams: “This can be an inspiration on looking for a source of an issue.”
In diverse teams, misunderstandings and communication problems are normal. But for Lydia, this is an opportunity, because she knows that there is one thing that everyone has in common: “This is the passion for data. (…) We are not very diverse in what I kind of like to call an ‘engineering mindset’.” For each team member, it’s about finding a machine-learning solution that is suitable and solves a problem. This is exactly where everyone pulls together for the overarching goal.
“So in the topic that matters the most for us is to deliver. We have a common goal, common understanding, and a common idea of what we like to achieve. “
“I believe people need to have clarity on what is their responsibility, otherwise they cannot take ownership of it.”
Due to the diversity in Data Teams it’s also important to clear the responsibilities. For Lydia, everyone on her team must know what they are responsible for. So that they can be contacted if there are any questions or problems.
Listen to the entire interview on the Data Culture podcast and learn how she finds the right people for her team and whether data engineers play a major role in the team.