Love the idea of trying to understand Data PM roles by breaking down job descriptions! This role in particular is very interesting as it feels like it overlaps with analytics/data science leadership vs data product management.
IMHO, based on the role description and requirements it feels more like they need a data "advisor" than a PM. I would argue that a significant part of the responsibilities of this role could be performed well by experienced data analysts/scientists working closely with the Ads Platform PM. A Data PM could be brought in when they need to build features that leverage data/predictions that can help improve the performance of the Ads Platform itself. Or if the data collected by this platform is important enough for the broader organization that it needs to be treated as a product in itself.
That way, the PM would be empowered to own a problem space and have the resources to build solutions to solve them. Either way, this is role is a great example of how "data product management" can be pretty abstract and why the nuts and bolts matter.
Love the idea of trying to understand Data PM roles by breaking down job descriptions! This role in particular is very interesting as it feels like it overlaps with analytics/data science leadership vs data product management.
IMHO, based on the role description and requirements it feels more like they need a data "advisor" than a PM. I would argue that a significant part of the responsibilities of this role could be performed well by experienced data analysts/scientists working closely with the Ads Platform PM. A Data PM could be brought in when they need to build features that leverage data/predictions that can help improve the performance of the Ads Platform itself. Or if the data collected by this platform is important enough for the broader organization that it needs to be treated as a product in itself.
That way, the PM would be empowered to own a problem space and have the resources to build solutions to solve them. Either way, this is role is a great example of how "data product management" can be pretty abstract and why the nuts and bolts matter.