Members of your data science team should be familiar with various forms of data anonymization. Depending on the nature of your data, it usually involves removing or obfuscating all data that has the potential to identify a person/client/other. Feature scaling, encoding, and name swapping (as @I_Play_With_Data had mentioned) can help reduce the possibility of revealing personal data or identifying the input source (individual persons or other entities).
While there's usually data that can be dropped or obfuscated entirely without impacting the results (like encoding or removing a client's SSN from a dataset), there are often features which are more difficult to handle in a correct manor. If you decide to encode categorical data, there are multiple ways that this can be done and the data scientists will need to be made aware of any assumptions made during the process (i.e. that null values were encoded or that a certain set of columns represent a single original feature). There are a number of things that can go wrong if you're too aggressive in your attempts to anonymize data, so often it's best to delegate the task to people with experience on both ends of the business, for example: a member of the compliance department familiar with data science or a member of the data science team who is also a subject matter expert.