This blog is contributed by Heidi Kalish and Matt Kleffner, Burtch Works’ analytics recruiting experts that specialize in the financial services industry
Earlier this month, Burtch Works’ Managing Director, Linda Burtch, posted her list of predictions for the 2019 data science and analytics hiring market. With over 30 years of experience recruiting quantitative professionals, her annual predictions list offer a great vantage point into trends that will shake up the market over the coming years.
Since the financial services industry employs a significant number of analytics and data science professionals (as our annual salary reports have found), this year we thought it would be interesting to take a closer look specifically at trends in this space.
Burtch Works’ Predictions for Data Science & Analytics in Financial Services
1. R and Python usage will continue to increase
While it’s no secret that many financial services organizations are still using legacy tools including SAS, our SAS vs. R vs. Python flash surveys have also found that financial services professionals in particular tend to favor SAS. More recently, however, we’re seeing more financial services firms moving their data science and analytics teams over to R and Python.
Banks and other organizations looking to land top data science and analytics talent would be wise to adopt newer tools, since lack of access to newer technologies is a common pain point we hear from financial services professionals exploring new opportunities.
2. Junior SAS experts will become increasingly harder to find
A deeper dive into our survey data shows a clear trend of early career professionals strongly gravitating towards open source tools like R and Python. We’ve seen numerous financial services organizations searching for and struggling to hire junior SAS experts, because most data scientists and analytics professionals are rarely learning SAS in school. If someone learns SAS in school it tends to be very school-specific, for example, if one of their professors used to work for SAS, rather than the widespread university adoption necessary to produce enough junior SAS experts to meet the demand.
3. The center of excellence model will become more attractive to candidates in financial services
Whereas previously data scientists and analytics professionals in financial services organizations might remain siloed and limited in the tasks they tackle (only tackling credit risk functions, for example). We’ve noticed more companies adopting the center of excellence model, which centralizes professionals on the data team and allows them to touch a variety of specialties within data science and analytics. Strong talent tends to be motivated by the desire to tackle interesting problems, so adopting a more centralized model can increase task variety and make opportunities more appealing.
4. More financial services firms will embrace unstructured data
We’re predicting that even more financial services firms will expand their data science capabilities by integrating more unstructured data. Pursuing cutting-edge tools and technologies is not only important for retaining top talent, it’s also becoming more necessary to keep pace with the competition.
5. Data scientists and analytics pros in financial services will become more well-rounded
Following along the lines of predictions 3 and 4, we’re predicting these two trends will combine to encourage quantitative professionals to become more versatile. More financial services institutions are integrating approaches like machine learning and deep learning into their data science teams, and this is encouraging analytics professionals to expand their skillsets.
Although it’s still possible to be a Credit Risk Analyst and only do that, we’d caution professionals that the longer you spend specialized in a single area the harder it may be to transition to something else if you decide that’s what you want to do! Analytics professionals looking to shift into data science positions have numerous options now, and learning more tools and techniques makes you more marketable!
6. Employers will prefer for managers to stay hands-on
We’ve pointed this out before, but now more than ever companies are looking for data science and analytics leaders who remain hands-on with the data. Especially in smaller or expanding teams, many financial services organizations nowadays are looking for leaders who can be a “player coach” – who can mentor the growing team and get their hands dirty when necessary.
On the flip side, we’ve seen many candidates who, after pursuing their advanced degrees, want to keep coding in addition to filling leadership roles. We’re seeing more leaders coming up through the quantitative ranks as opposed to Finance MBAs who understand just enough about the data to get by.
7. Unless premium processing returns, there will be a shortage of top talent
With premium processing for H-1B and other visas suspended (and fees increasing), companies have been more hesitant to sponsor visa candidates and therefore, financial services organizations are struggling to fill positions. Premium processing allows the visa petition process to be shortened significantly by paying a fee, and without it there have been considerable delays in the hiring process. Data science and analytics in particular are fields with many visa candidates, and unless premium processing returns this could present significant challenges to companies looking to have access to the entire talent pool.
8. Increasing opportunities for data scientists and analytics pros in investment banking
Opportunities for quantitative professionals in investment banking are continuing to pick up steam. More companies are integrating machine learning and analytics strategies for investment and trading strategy, with data scientists charged with creating automated systems for traders. As financial services as a whole takes steps further into the data science realm, we predict there will be even more opportunities for those looking to branch into the investment banking space.
Emerging Industry Spotlight: Cryptocurrency and Blockchain
One area to watch this year is the increasing buzz around cryptocurrency and blockchain. Although it’s too soon to say what will become of opportunities in this fickle industry, we’re seeing more companies hop on board and experimenting with cryptoassets, so this is an area we’re keeping on our radar in 2019!
We 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 (Heidi Kalish and Matt Kleffner) on LinkedIn.
Learn more about the 2019 trends we’re keeping an eye on for data science and analytics in financial services in the video below!