This blog is contributed by Burtch Works’ data science & analytics recruiting team that specializes in the financial services industry.
A few months ago we posted a blog with our 2019 predictions for analytics & data science in financial services, one of which was that the Center of Excellence model for quantitative teams would become more attractive to candidates in this space.
Recently I’ve had several interesting conversations with professionals and companies about this growing trend, so I thought I would dig a little deeper into how this trend is taking shape across the industry.
What is the Center of Excellence Model?
For those who are not familiar, the Center of Excellence model is a team structure which places the data science and analytics team as its own department (not just centered under marketing, for example), where professionals can serve multiple areas of the business (such as Marketing, Finance, Operations, etc.).
This differs from a siloed approach where each business unit would have their own data scientists and analytics professionals (some under Marketing, some under Finance, etc.) that focus specifically on that area but don’t usually cover all the business units.
Advantages of the Center of Excellence Model for Financial Services
One of the key concerns, in my experience, for data scientists and analytics professionals in this industry is being able to pursue cutting-edge technology and tools, and I’ve written before about how professionals can manage this challenge.
Another related concern is being able to work on different analytical processes and keep their experience/skillset diverse, rather than focusing purely in a single area, such as credit risk. For professionals, the advantage to the Center of Excellence model is that it allows them to tackle many different problems and expand their knowledge. For employers, it can offer the opportunity to market this benefit to potential talent, and also prevent your current staff from feeling stagnant.
From my recent conversations, here are a few of the common themes I’ve heard:
1. Developing a Center of Excellence can offer a route to a Chief Analytics Officer position
For organizations that still house their analytical talent under multiple business units, a CAO position might not necessarily make sense. However, if you’re looking to develop yourself as a cross-functional leader, the Center of Excellence can make a strong case for adding this position, since having this team structure requires someone who is both an advocate for analytics within the organization and someone who can manage the team.
I spoke with one analytics leader recently whose analytics team had started out mainly supporting the marketing function within a bank, but who eventually was able to grow the team and its use cases to support other areas of the business and eventually expand the department into a Center of Excellence.
2. Limiting your analytics team can encourage talent to look elsewhere
At Burtch Works we often emphasize that diversifying your experience is the best way to stay marketable in an industry that is constantly evolving. Strong talent often know this, and I’ve spoken with many professionals recently who were on the market specifically because they felt their organizations were not allowing them to grow their skills.
Whether it’s expanding your team’s tool usage to include Python or using a Center of Excellence approach to let them tackle a wider variety of problems, changing things up keeps things interesting. The analytics and data science professionals I talk to almost always want to solve interesting problems instead of performing the same tasks repetitively, and letting them support a variety of business units can be a great way to address this.
3. A Center of Excellence approach can take many forms
Aside from the traditional Center of Excellence model I outlined previously, there are several ways I’ve seen similar approaches take shape that can serve the same purpose of allowing quantitative experts to work on different problems:
Private equity firms that own several companies might have a centralized analytics team that they can then dispatch to different businesses that they own, functioning like internal consultants.
Allowing professionals to take on projects for different teams can give them the opportunity to expand their knowledge without necessarily upending your organizational structure.
Business process outsourcing firms can also be an opportunity for professionals looking to apply their analytical skills to a variety of industries without branching into full-on consulting work.
Keeping Skills Diversified and Staying Hands-On is Key
However you choose to approach it, the key to keeping analytics professionals and data scientists happy is to give them opportunities to learn and expand their skills. They know that the longer they stay specialized in one area, the harder it is to transition out of it, so many are beginning to preempt career stagnation by looking for new opportunities if their current employer doesn’t offer chances to expand their knowledge.
And, with tools like R and Python increasing in popularity and more financial services firms embracing unstructured data, we’re seeing increased demand for hands-on managers. Some firms are requiring technical screens even for senior-level management positions, and I’ve spoken with several candidates with proven management track-records who were let go during the hiring process because they didn’t pass technical screens.
The best advice I can give both employers and professionals in this space is to stay aware of emerging trends in the data science and analytics space and be adaptable. After all, if Goldman Sachs can relax their dress code, anything can happen!
I hope you found this information helpful! If you’re looking for analytics or data science opportunities in the financial services space, or looking to add to your team, be sure to connect with us.
Learn more about the 2019 trends I’m keeping an eye on for data science and analytics in financial services in the video below!