Financial institutions are identifying uses for AI throughout their operations, creating increased demand for data scientists in the financial services industry.  

“We’re seeing a rapid push into the hiring of data scientists,” Kevin Green, chief product and marketing officer at software development company Truent, told Bank Automation News.  

A data scientist combines math, statistics, programming, analytics, AI and machine learning to understand actionable insights for an organization — in this case, financial institutions.  

Employment of data scientists is projected to grow 35% between 2022 and 2032, according to the U.S. Bureau of Labor Statistics.  

Financial institutions are contributing to this uptick, according to Deloitte’s 2024 Banking and Capital Markets Outlook report. FIs are working to attract talent across the following areas this year: 

• AI; 
• Cloud; 
• Data science; and 
• Cybersecurity.  

The $2.5 trillion Bank of America, $476 billion Capital One, $2.4 trillion Citigroup, $3.9 trillion JPMorgan Chase and $1.9 trillion Wells Fargo are all hiring data scientists, according to job boards on their websites.  

In 2024, data scientists are expected to be the No. 1 recruited profession as banks start to invest heavily in the talent and resources they need to be competitive in their approach to AI, Green said.  

But banks need more than data scientists. They need data analysts, data infrastructure and AI leadership.  

Additionally, according to an October study by Google Cloud, which surveyed 350 banking executives, banks are also looking to fill the following roles: 

• AI quality assurance testers; 
• AI strategy consultants; and  
• AI product managers.  

Top-down approach

FIs need to have their entire operations on the same page on utilizing the power of AI, Green said. 

“You can’t push these AI roles into the engineering department, you need a top-down approach,” he said. Everyone in the FI must understand its AI strategy, from the chief executive to the engineering and compliance teams. 

AI “has to be an executive role,” Green said. 

U.S. Bank is implementing AI throughout its organization, and has named Srini Nallasivan its chief AI and analytics officer. His title had been chief analytics officer. 

Nallasivan has been with the bank since 2018 and understands how to use data to gain insights and present opportunities, according to the U.S. Bank website.  

Chief Technology Officer Michael Palmer also specializes in AI, ML and data technologies, according to his LinkedIn profile.  

AI is not just a tool to be implemented, it is fundamentally changing the business model of bank operations, and leadership teams must be involved, Green said.  

Human in the loop

As FIs look to invest in AI, one concern has been whether AI will replace people and jobs, Sameer Gupta, principal at EY, told BAN. 

And the answer is quickly becoming clear — no. 

In fact, “there is more demand than ever from AI practitioners,” Gupta said. 

Barry Baird, head of payments capability and delivery at TD Bank, agreed. AI may change the skill sets that bank staff must have, he said, but humans are still a necessity in the AI overhaul at banks. 

AI just spits back data patterns; humans are needed to interpret the data, monitor false positives and keep AI in check, he said. 

Bank employees will have to have the skills to do those things, he said. While people won’t be out of jobs due to AI, “you’re just going to have to have the skill sets that are challenging what [your institution is] getting out of the AI results,” he said. 

Expanding the talent pool

How are FIs identifying AI talent hires? 

Experts shared three ways FIs can expand the AI talent pool:  

1. Look outside the banking industry; 
2. Look at a variety of geographical locations; with remote work and accessibility readily available, banks should be looking to hire talent beyond their immediate footprint; and 
3. Look for opportunities to train current employees in AI. 

FIs can expand their talent searches to the e-commerce pool of people who are looking for their next challenge, Green said. Not necessarily Amazon or Walmart, but also people from Dell or similar companies. 

Banks are sitting on all the same data that e-commerce companies are, but bank data is “far more robust,” Green said, noting that banks know transaction history, payment information, buying preferences and frequency of purchase.  

The biggest problem with bank data is banks don’t recognize the value of what they have, he said. 

Banks can take the same approach to cross-selling that e-commerce companies do if they use the data they are already collecting, and if banks bring in experts who have experience doing that, profitability will climb, Green said.  

Internal training

According to an EY report, which surveyed 300 executive directors at financial organizations in August 2023, “44% of leaders cited access to skilled resources as a barrier to AI implementation, but there’s only so many already skilled professionals in existence. Part of the solution is deploying upskilling programs today that can equip your current workforce with skills they need to help leaders and their business thrive in an increasingly AI-centric world.” 

Citizens Bank is introducing a data academy so employees can learn more about AI and ML, Chief Information Officer Michael Ruttledge told BAN. The hiring market is “tight,” he said, noting that bringing in vendors and outside talent and training existing employees is the strategy the bank must stick to for now. 

The bank is training and teaching its data engineers to be AI-literate, while searching for new hires, he said.  

Now hiring

As FIs look to add to their AI talent arsenals, the following job postings are live now: 

• Citigroup: Director of generative AI, Innovation Lab 

Citi is hiring a director of generative AI within its innovation lab, according to its website. 

The ideal candidate is an experienced senior data scientist in natural language processing understanding and generation, according to the job posting. The selected candidate will drive innovation for generative AI at the bank.  

Qualified applicants must have a doctorate, master’s or equivalent degree in computer science, machine learning or a related field, 10-plus years in ML, knowledge of open-source libraries and APIs, proficiency in programming languages and contributions to AI research.  

Compensation is listed at between $200,000 and $300,000.  

• JPMorgan: Applied AI machine learning analyst 

JPMorgan is hiring for its applied artificial intelligence and machine learning analyst role within its Corporate and Investment Banking business, according to its website. 

The selected candidate will be responsible for the development, testing and deployment of machine learning applications, according to the job posting.  

Applicants must have a bachelor’s degree in computer science, mathematics, engineering or a related field, as well as experience in programming applications including Python. 

Compensation is listed at between $99,750 and $135,000. 

JPMorgan posted this job Feb. 14. 

• Wells Fargo: Head of data capabilities, Corporate and Investment Bank 

Wells Fargo is hiring a data management director and head of data capabilities for its Corporate and Investment Bank, according to a job listing.  

Responsibilities include the development and deployment of advanced data tools, evaluation of new data technologies, third-party solution integration, implementation of AI and ML and more, according to the job post.  

Qualifications include eight or more years in data management, four or more years in management and experience in managing and developing data tools, AI and ML. 

Compensation is listed at between $144,400 and $300,000. 

This position was posted by the bank Feb. 13. 

Monetizing data

A lot of financial institutions don’t understand the value of the data they’re sitting on, Green said. 

“The right person inside some of these banks will fundamentally change the product offering of financial institutions,” he said.  

Data scientists can take rich, diverse sets of data that banks already have, and build profiles from the data to identify cross-selling opportunities, Green said. These individuals cost a lot of money to hire, however, “They are going to generate millions in revenue.” 

“With the right people building the right profiles and capturing the right data, the economic opportunity allows [banks] to create a more diverse revenue stream that is going to give them more stability,” Green said.  

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