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This blog is contributed by Heidi Kalish and Matt Kleffner, Burtch Works’ analytics recruiting experts that specialize in the financial services industry


While quantitative talent has long been prized in the financial services industry, as the hiring market for analytics professionals and data scientists heats up the competition has gotten fierce!

Particularly in larger financial services companies, there are some unique challenges employers may face when looking to attract top talent to their organizations, so we thought we would address ways to tackle them so that you can ensure you’re landing top talent.

1. Realize that it’s an active market and candidates will be moving quickly

Although we recognize the need for following your hiring process, it’s also important to note that if you move too slowly while interviewing candidates, you may lose out. Candidates are not staying on the market long, and especially when you’re competing with more nimble companies or Fintech startups, a lengthy process can work to your detriment.

Larger banks may be more regulated and be working against more approvals and red tape, so if you aren’t able to speed the process it’s important to accurately set expectations and be clear about next steps. Being clear about a candidate’s interview progress helps keep them engaged and can help quash any doubts that might arise if they haven’t heard from your team for a period of time.

2. Extending strong offers can help you stand out

Along those same lines, an active market often means that candidates will be evaluating multiple offers, so putting together a strong offer is incredibly important. Analytics professionals in financial services have typically been highly paid, but with the rise of data science there is increased competition for talent across the board.

3. Consider adding perks aside from salary and bonus

In addition to offering a strong base salary and bonus, consider adding other perks that might set you apart. As we saw in our flash survey earlier this year, topics like work/life balance and remote/work from home options can be very appealing to analytics professionals and data scientists considering a job change.

We’ve seen larger banks offering options like giving analysts the option to work from home one day a week, or Fintech startups bringing in lunch for the team every Friday. The desire for good work/life balance is something we hear about a lot in our conversations, especially with analytical talent in financial services.

4. Strong analytics and data science talent want to answer interesting questions

The strongest analytics and data science talent that we speak with here at Burtch Works often say they want to be answering interesting questions or to feel like they have an impact on the business. This can especially be a pain point in financial services, where larger teams may be siloed between areas and professionals feel like they’re tackling the same problems or tasks (like report generation or data cleaning) over and over. In this sense, Fintech startups can be especially appealing because they offer different approaches.

Be innovative about how you’re using your data or what business questions you’re answering with it. Allow for an environment where it’s encouraged to think creatively and outside the box. We’ve also seen some financial services clients rotate their quantitative team members through different functions so that they’re given the opportunity to exercise more of their skillset. Or, a company may choose to create an analytics center of excellence, which can help employees get more experience by allowing them to touch many projects.

Departments across a company can request the Analytics Center of Excellence for support with their business question. In cases like this, a professional might work on various project within a year, e.g. predicting refinancing rates of assets, evaluating risk of branch robbery, estimating effects of branch consolidation, or optimizing business placement.

5. Don’t be afraid to try new tools and offer learning opportunities

In our recent SAS, R, or Python flash survey, we found that open source tools R and Python together are more strongly preferred then SAS for analytics professionals and data scientists in financial services. And, when examining tool preferences by years of work experience, it’s clear that professionals with 10 or less years’ experience clearly favor R or Python over SAS.

We’ve seen many analytics and data science professionals on the market simply because they don’t feel challenged and want access to cutting edge or newer technology. In fact, the desire to learn is such a common refrain from financial services talent that we covered the topic in a recent blog. We’ve had candidates tell us they set up internal training at their company to teach R or machine learning techniques. It’s attractive to candidates for there to be resources in the company that will help them learn and develop their analytical/statistical skills.

6. Junior candidates especially will be looking for mentors

For early career candidates in particular, we often hear they want to be in an environment where they can continue to learn and grow, so it’s important to have leaders in place that are accessible. Offering access to mentors or experienced quantitative leaders that are invested in helping to develop their team can be very attractive to top talent!

 

Overall the market for this talent is strong, but the tips above can help you impress potential candidates and land the analytics and data science talent you need for your organization.

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.

Interested in our salary research on data scientists and predictive analytics professionals? Download our studies using the button below.

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Watch our 13-minute RECAP video below to find out the latest salaries, demographic data, and hiring market trends impacting both data scientists and firms looking to hire them!

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