A data scientist conditions data, creates models, and tests and evaluates their effectiveness. The role addresses challenges spanning data, modelling, and domain alignment within an ML project team. section 3.2.2
Place the role alongside complementary specialists:
- Data engineers handle data resources, platforms, tooling, security, costs, and efficiency. section 3.2.2
- ML engineers manage model infrastructure, versioning, testing systems, and evaluations. section 3.2.2
- Business translators or product owners connect modelling work with users, stakeholders, and domain needs. section 3.2.2
Figure 3.2 Team roles created by the three drivers of complexity in ML projects
Use the team-role map to distinguish data-science work from the data, engineering, and business responsibilities surrounding it. section 3.2.2
