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This blog is contributed by Heidi Kalish, Burtch Works’ data science & analytics recruiting expert that specializes in the financial services industry

As tools go, SAS has long been a mainstay for analytics and data science teams in financial services. One of the topics I had written about last year, however, was how many professionals have felt the desire to expand their skillset towards open source tools (like R and Python). Depending on their organization’s investment in legacy systems, using new tools at work can present a challenge.

This strong desire among quantitative professionals to expand their skills affects employers as well, since offering the latest tools, technologies, and opportunities to learn is swiftly becoming one of the keys to landing top talent in financial services.

Although there hasn’t been a complete upheaval in the industry yet, I have been noticing several trends this year that indicate change is afoot. Since I have a perspective into many different financial services firms through my recruiting conversations, I thought I’d share some of them with you.

1. More financial services firms are shifting towards R and Python, or at least allowing them

While transitioning away from legacy systems can present a significant hurdle, many data science and analytics teams at financial services firms are doing just that. I’m seeing more organizations starting to make this transition, or, if they’re still committed to using SAS, many are at least allowing professionals to use R or Python if they choose.

This comes at least partly from needing to lure top talent, and because there isn’t a huge commitment or cost associated with open source tools, it can be easier to allow professionals to make their own tool choices rather than searching exclusively for SAS experts.

2. The shortage of junior-level SAS experts is only becoming more acute

One of my predictions from earlier this year was that early career SAS experts would become increasingly harder to find. One of the driving reasons for this is because less students are learning SAS while they’re in school, so early career professionals tend to be more likely to prefer R and Python.

Since hiring junior-level SAS professionals has become increasingly difficult, this has prompted many teams to accept other tool proficiencies in order to expand their potential talent pool (see point #1).

3. Some firms are even passing on analytics professionals who only know SAS

Despite the fact that many financial services organizations are still using SAS, I believe a strong indicator of where things are headed is the fact that some of these organizations have been turning down analytics professionals who exclusively use SAS. Many of these firms want quantitative professionals who are more well-rounded, open to learning new technologies, and preferably have a broader array of tools at their disposal.

In one case, a candidate who had both SAS and Python skills was passed over because when, in the interview, she was asked whether she was open to learning more Python, her response was that she preferred to use SAS. While the preference for Python may not necessarily be the case at every financial services firm, most data science and analytics teams now are looking for professionals who are open to growing their skillset and learning new tools and technologies.

4. More financial services firms are looking into the implications of tool transition

I recently spoke with a senior-level data scientist from an insurance company who is often asked to speak at conferences on the topic of compliance and regulation in regards to implementing new tools. He mentioned that there has been a surge in interest from companies in this space who are, perhaps feeling a bit behind, now starting to ask how they can transition and best practices for staying compliant while using tools like R and Python.

5. Data science evangelism is key to selling the shift to new tools, and analytics in general

Especially at the senior-level, being able to see the bigger picture and work with other business unit heads to evangelize analytics is a crucial skill. This is true whether you’re looking to sell a shift in tools, increase the use cases for analytics at your financial services organization, or even experiment with how emerging technologies might fit.

Even for early career data scientists and analytics professionals, learning how to communicate the value of analytics in the context of someone else’s business priorities is a skill that will serve you very well!

 

 

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 me on LinkedIn.

Learn more about the 2019 trends I’m keeping an eye on for data science and analytics in financial services in the video below!

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