Data engineers and data scientists operate under different constraints. Engineers want stable, testable, high code-quality codebases. Data Scientists want flexibility and sandbox environments to easily test their hypotheses. These differences can exacerbate the already difficult inter-team dynamics involved in building a joint product. While working for the first time as a cross-functional team to build a data application, we had to deal with the following issues:

• How much documentation should the data scientists provide the engineers about the model

• Reconciling differing interpretations of business logic between teams

• Reimplementing code from IPython notebooks into an abstracted, object-oriented application

• Predicting future usage of the application given only the current use case

• How to allow the data team to easily experiment and directly reimplement functionality without necessarily going through the full engineering code review process

This talk will discuss how those problems arose and how the data science and engineering teams solved them while creating a new application.