How high-performing data science teams operate
Very few teams operate at their maximum potential, and even with a team of highly qualified individuals, if there is ineffective collaboration, stifling bureaucracy, or inadequate tooling, the project can break down.
The following are some guidelines on what it takes to run a high-performing data science team.
Cross-functional collaboration is essential
The most impactful DS/ML/AI projects involve close partnerships between data scientists, ML engineers, software developers, product managers, designers, and subject-matter experts. Fostering a culture of collaboration and breaking down silos between these functions is critical.
Diversity of perspectives drives innovation
Top teams bring together people from different backgrounds – not just in terms of demographics, but also academic training, industry experience, and ways of thinking. Cognitive diversity helps teams approach problems more creatively.
