This chapter covers
- Working with different types of stakeholders
- Engaging with people outside the data science team
- Listening so that your work gets best used
It seems like the job of a data scientist would be primarily about data, but much of the work revolves around people. Data scientists spend hours listening to people in their company talk about the problems they have and how data might solve those problems. Data scientists have to present their work to people so they can use the knowledge gained from an analysis or trust a machine learning model. And when problems occur, such as projects being delayed or data not being available, it requires conversations with people to figure out what the next step should be.
Karl Weigers and Joy Betty define the term stakeholder in Software Requirements as “a person, group, or organization that is actively involved in a project, is affected by its outcome, or can influence its outcome.” For a data scientist, stakeholders can be the businesspeople working in marketing, product development, or other areas of the business who use data science to make decisions. Stakeholders can also be people in engineering who rely on machine learning models created by data scientists to power their software or make sure the data is collected properly. In some situations, stakeholders are high-level executives. Stakeholders can come from across the company, and different stakeholders have different behaviors and needs.