Any medium or Large data firms should have one or more Data Science Manger who is mainly responsible for building and coordinating data science team. He manages the overall flow of the data science process and team. He is last and top member of the data science team.
Content Overview
They are responsible for identifying and recruiting data engineers and data scientists to the team; then getting them set up, setting goals and priorities, identifying those problems within an organization that need to be solved by data science, and putting the right people on the right problem.
Key Responsibilities
- Building the data science team
- Identifying and recruiting data engineers and data scientists to the team;
- Setting goals and priorities for the data science team
- Identifying those problems within the organization that need to be solved by data science team
- Putting the right people on the right problem and get them solved by guiding and motivating the team
- Managing the whole data science process.
One of the manager’s responsibilities is making sure that people routinely interact with each other within the data science team. And also interact with other people in the wider organization. The data science team may also interacts with other groups, so the manager might report to some higher managers. The manager might also just interact with, or collaborate with people at their same level and other units of your organization, and so they need to have good communication skills in that sense.
What kind of skills does a data science manager need?
To be data manger you need to have at least these following skills and experience.
- Experience in data science
- Experience in data Analytics or Business Analytics
- Knowledge in data engineering
- Experience in management
- Knowledge of the business
Ideally they should have knowledge of the software and hardware being used for the specific problem. It is really great if they have some kind of background of data science or data engineering. Sometimes, it is sort of the ideal case. I am telling this is ideal, because they actually know the infrastructure that’s involved. If there’s a problem comes up they really need to give a good suggestion about how to fix the data science infrastructure, or how to fix that machine learning algorithm that doesn’t necessarily work exactly like the person wanted. They don’t have to have that qualification, but it’s nice if they have it.
The manager does need to know each and every roles in the team. They need to know what a data scientist does, what a data engineer does, what other teams are supposed to be doing, and how that may or may not be data science. They need to filter out the problems that aren’t necessarily appropriate, and focus on the ones that are. They need to know what can and can’t be achieved. Data science is useful and it can be a very powerful tool for an organization, but it’s not all purpose and all knowing. And so, there are often problems that just can’t be solved with data science. That could be because the data aren’t available. It could be because algorithms aren’t good enough to be able to do that kind of prediction at that time. It could be because we can definitely solve the problem, but we just can’t scale it up to the scale that’s necessary. Manageres need to kind of have an idea of what are the global parameters of what can be done and what can’t be done with data science.
What is the background of data managers?
Data managers basically come from some sort of data science background. Whether that’s analyzing data themselves, or building data science infrastructure themselves, plus some management training. Another option is that they should have some management experience, but they’ve also taken or learned a little bit about data science and data engineering.