This blog is contributed by Burtch Works’ analytics recruiting team that specializes in the financial services industry.

As analytical methods continue to make their presence felt in the financial services industry, I’ve noticed a growing trend among analytics professionals and data scientists that I speak to. Increasingly, many of these job seekers, who are quantitative specialists working in financial services, have expressed that the main reason they’re on the market is because they feel their company is not doing enough to keep up with changes in analytics.

The Struggle to Stay Current in Financial Services

A common refrain that I hear from professionals I work with is something like, “My company isn’t cutting-edge, or even trying to keep up with new developments, and I’m afraid that if I don’t keep up with all these changes, my skills are going to be completely out-of-date.”

This professional’s company might exclusively use SAS for analytics because that’s what they have in place and it would take a lot of time and money to change that, but as a result these professionals feel like they aren’t able to learn some of the newer technology that is becoming available. Not only does this have the potential to stymie a professional’s career growth, but when they finally do go on the market, their skills might not be as marketable anymore.

This also puts companies in the position of losing out on the rewards that more innovative analytics approaches can add to the bottom line, and puts them at risk of losing top analytics and data science talent. With the buzz surrounding US FinTech companies creating an uptick in investments, traditional financial firms that are resistant to change their legacy systems risk being left behind, and quantitative professionals are taking notice.

Where to Find Innovative Analytics and Data Science Approaches

Although it may seem like the obvious place to start looking, FinTech is not necessarily the only place to find cutting-edge analytics in the financial services industry. There are some larger, more traditional firms that are incorporating machine learning and artificial intelligence into their approaches, and adopting newer tools like R and Python, however the increasing competition for talent has made the market for quantitative talent a challenge for everyone.

There are also other potential places to look for opportunities besides the large banks, including hedge funds or investment firms and other smaller finance companies. For example, investment firms might hire analytics professionals to integrate alternative data sources and predictive modeling to drive investment strategies, or a finance company might seek to hire data scientists to expand beyond the standard credit scoring model.


What You Can Do to Stay Sharp

In the past, Burtch Works has spoken with FICO’s Chief Analytics Officer, Scott Zoldi, about innovation in financial services, as well as how analytics professionals can keep their skills sharp. We’ve also spoken with Jason Dell, a FinTech insider with experience at many new and younger financial services companies, to get his thoughts on how to evaluate the risk vs. reward of joining FinTech startups.

Both interviews are worth a read if you’re looking for advice from experienced quantitative professionals about your own financial services career, but here are some additional points I would add:

1. Don’t be afraid to learn things on your own!

With so many online resources for learning (including Coursera, DataCamp, Udemy, and Udacity, just to name a few), I always encourage professionals to look into self-learning tools if you feel your current company isn’t giving you the resources you need to advance your skills. Our latest SAS, R, or Python flash survey shows that tool preferences have definitely been shifting over the past several years, and learning on your own can be a great way to adjust to these changes.

2. Participate in Kaggle competitions or other ways to test your skills

Putting new skills into practice is always a good way to demonstrate that not only have you taken a Python course, but you can also use it as well. Cultivating a GitHub profile can also give you an advantage when interviewing.

3. Bootcamps can be an option if you need a more immersive skills update

If you’re looking for a more comprehensive skills update or retraining option, bootcamps can also be an option to look into. This post compares Master’s programs, MOOCs, and bootcamps as three potential approaches for learning, and the benefits/drawbacks of each.

4. Stay caught up on analytics and data science news and developments

In addition to learning technical skills and trends in financial services, it is also wise to look outside the industry to see what developments are taking place in other industries as well. Pay attention to what is happening with analytics and data science in research, technology, and other areas, especially if it’s an industry you may want to transition into!


Overall, it’s an exciting time to be working in financial services as the industry looks to adapt new tools and technologies. Continuous learning and career development is something we emphasize to all analytics and data science professionals that we work with, regardless of industry, especially as the field is evolving so rapidly. It will be exciting to see what changes the next few years bring!


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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 we’re keeping an eye on for data science and analytics in financial services in the video below!

4 Responses to “The Challenge for Data Scientists and Analytics Pros in Financial Services”

  1. Phillip D Julian

    Financial services seem to pigeon hole individuals into narrow roles, which also makes it difficult to practice new programming skills — even when that work is done in the same department. Your suggestions in the article don’t mention getting on-the-job experience, so I think you may be aware of this issue. It seems like a great idea to grow talent from the inside out, if you can find support for a bold and liberal approach to enhancing talent.

  2. Low Weng Haw

    I think the challenge for this data scientist is not the technology is the ability to identify the problem statements and provide solutions based in the analytics from data coming from relevant sources. Empathy is also a very important competencies to have couple with ability to tell a good story in layman terms for their suggested solutions. It must NOT be a statement of facts with no clear problems statements, options and proposed solution.



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