Skills and Specializations in Data Science Recruitment: A Deep Dive into the Qualifications and Roles Needed for the Future of Analytics

Great. So when are you recruiting the next group of this data scientist? And what do they do look out for? What must they, what do you look for you to be able to increase.

This youngster? I mean, that is scientist and the quantitative hiring that is part of the wider the, you know, you know, we, we have, we’re hiring across disciplines, but this is the area that I guess I’m most impacted by. So I’ll speak more in terms of that.

We are really looking at, typically at people that have completed a four year degree is preferable. So if you are doing a three year degree, do honors as well. That allows you to pick up more skills. And often we are also even interested in people with a masters program. Okay, not that much PhD, because I think there’s just a few of those. But especially now in these fields, some of the topics are becoming more complex and having some deeper skills and a bit more experience is very useful when we hiring in my field. I mean, typically mathematics, statistics, it’s more about the courses, whether you do those courses as part of studying engineering, actual science, a computer science degree will just give you a different angle on it. If you’re coming from computer science, students often better at programming, they come engineering, they can think more design thinking. And especially now with, let’s say in AI, you’re programming an agent that is basically, can be basically using multiple sets of data with different large language models, etc. So that’s the kind of design thinking is is more important. Or if it’s actual, often actual students are very good at risk management. So that’s a big part of what they do in actual science. So often, if somebody is going to go into more a credit analytical field or is doing managing the risk of solutions maybe that other people also designed. But we, I think one thing needs to be careful not to type cost and say if you study, this is only what you can do. Okay, but there is a range of skills. And then the teams are also quite interdisciplinary. So you don’t need five people with exactly the same skill. You need a set of team. So you would typically want some data scientists and some of the data scientists might be some might be better at actually profiling and understanding data, some may be better at modeling. Then you need data engineers that are typically a lot better at understanding how they will schedule the tasks, how they will get the information to flow safely and securely and understand encryption, etcetera, you know, from one area maybe into like a private virtual cloud environment and how the data, you know, would come back from there, how we are putting decisions behind APIs. So, so application programming interfaces so that it can be easily accessible via multiple business areas that need to use the same function.

And then there is a lot of specialist roles that have developed. It’s almost, it’s not like these skills, these fields didn’t exist before. But I don’t know if you have heard of information architects, data modelers, solution architects. These are cloud engineers, AI engineers, machine learning, ops engineers. These are jobs that probably, you know, didn’t really have a lot of airtime five years ago where is now. In our organization, we have specialist areas. So we would have a lead information architect in the group, and then we would have lead information architects in each of the major areas. And data modeling is the same.

Okay, and it’s okay, I want you into detail explaining what these different jobs do, but there is a lot of specialization that people can do. And it is really, it means that we can cater for all kinds of. Some people, maybe they really like to work more with people. Other people, maybe they like to become more specialized in a given area and they’re not necessarily designing the solutions, but they’re helping other teams solve it. This program management is a big field as well, which is not really data science, but we’re finding that the program managers that we’re having, because they would run from a big program to lots of small little projects, and we’re running them in a, I don’t feel sort of agile is a new way that we’re developing things, which is a case trying to be much more flexible and delivering output in a way that is delivering value incrementally as it goes on, whereas the old waterfall style was somebody writes a business requirement and then it goes to someone else function, or then somebody develops code for maybe a year and then they give a product back and then people say, but that’s not what I wanted. That’s all the way of doing things. We’re trying to, you know, change to more paradigm. So they then there’s a change in even how you manage projects. We have scrum master, Instagram, Osters, got nothing to do with Rugby. Yeah, right. Is this the analogy? Is it like a scrum? So the scrum master is organizing that set of people that they can work together and that they have got, it’s a daily stand UPS and they’re delivering on what we call the, you know, cadence where they’re doing sprints every two weeks. They actually have got targets for every two weeks to release, which is very different from a traditional it set.