So, the way in which you'll interact with data science depends a little bit on what kind of organization you are. To some extent, it depends a lot on the size of your organization. So, when you're just a start up, when you're an early stage company, or you're just one person with a very small team, maybe like one other person working for you. You may not need to worry so much yet about how to do experimentation, how to do machine learning, how to do sort of prediction and downstream calculations. The first order of business is just making sure your sort of data house is in order. And the way to do that is to make sure you focus on infrastructure. So the first thing that you need to do is build out the infrastructure for storing the data, the databases and so forth. The software that's gonna be run to pull those data, the servers that are gonna serve the data to other people, and the servers that you'll interact with yourself in order to get the data out. So all that sort of requires infrastructure building up at first. So often the first people that you get to hire into a data science team, are not people that you would necessarily called data scientists in the sense that they're not analyzing the data, they're not doing machine learning. They might do a little bit of that but mostly they're involved on just making sure the machine is running, making sure the data's getting collected, its secure, its stored and so fourth. So when your a mid size organization, then hopefully you've got the basic infrastructure in place. And you can start thinking about building out your real data science team. And so to do that you can bring on board people that are actually called data scientists. And those are the folks who will then actually use the data. So they might pull it out of the database. They might run some experiments. They might build machine learning algorithms. They might analyze the data to see if you can identify any patterns or trends in behavior that you care about. And so at that point, you're thinking about actually building out the data science team. You're also thinking about implementing these data science ideas and products. So again, the data scientist might build something like a machine learning algorithm that predicts, say, consumer behavior. Once you have that algorithm built out, you might need to implement it back on to your system. And you might need to scale it up, so that it can be run on the whole data set. You might wanna build some sort of visualization, that people who aren't necessarily data scientists can interact with. And so, that would be turning it back over to the data engineering team. So there's still infrastructure concerns, because you have a large set of data that you've hopefully collected at this point. You need to be secure about it, you need to have a database, you need to be able to scale it. But, now you're sort of graduating into a more complete view of data science. For a large organization you have all those same sorts of things. You now have a data infrastructure, you might have a data science team that's running experiments. You may be using those experiments to make decisions. But now you have one additional component which is really managing the team and keeping everybody on task and coordinated. So at this point the data science manager role becomes a little bit more involved, in the sense that you might be coordinating multiple teams of data scientists working on different projects. You might have a team that works exclusively on building machine learning projects. You might have another team that works exclusively on running experiments and inferring what you can from those experiments. And then someone has to be in charge of coordinating those activities making sure they're connected to the right people within your organization. Whether that's the marketing team, the business group or whoever else that you're collaborating with. You have to be able to connect those people. And so at that scale the full data science infrastructure is in place.