Banks are working to fill talent gaps to compete in the so-called
“AI experts are an important part of this, because, obviously, they’re the ones who have the technical expertise,” said Ted Paris, head of analytics, intelligence and AI at TD Bank. “However, when we think about being able to actually deliver on these programs and what their promise offers, it’s not simply about doing the analysis [and] building the model. It’s actually about ensuring that you can effectively implement the model.”
An outcomes focus
For TD, this means a focus on outcomes. The bank needs AI specialists, data scientists and change management experts, but the hardest AI-relevant roles to fill are those that require risk-management skills, he said. These jobs are instrumental to the successful rollout of AI initiatives.
“That’s a space in which we probably have to lean in a little harder to find the talent right now [rather] than necessarily going out and finding the data scientists who are interested in actually building the model themselves,” he said. “Some of those [people] will be lawyers, but some of them are also technical people,” including those who assess and test models for consistency, bias and explainability, he added.
TD, which has
“There’s a spectrum of things that you can do within that space, but a lot of it is information extraction or knowledge management oriented,” Paris said.
Product management expertise
Smaller institutions that may lean more on vendors to build out their AI rollouts still may require in-house AI implementation expertise.
Ryan Hildebrand, executive vice president and chief innovation officer at Bankwell Financial Group, a $3.2 billion-asset bank based in New Canaan, Connecticut, said the bank is interested in adding product specialists who understand how to incorporate AI into product journeys.
“We’re not hiring engineers … we’re hiring people that can be really solid at understanding how a process might work and thinking about the end customer,” he said. “[They] understand things like compliance, operations, go to market, things like that.”
Hildebrand said he thinks there’s a shortage of product managers who have expertise in AI, as opposed to the more technical AI staffers.
“In the past, we’ve relied so much on engineers, and product managers have been kind of second. Now, I think things are flipping,” he said. This includes people who can work with AI and can consider what might be the best prompts, he noted.
Moving from ‘builders’ to ‘transformers’
According to Evident’s 2024
AI implementation holds a prominent place among AI roles tracked in Evident’s research from 2024. Among AI roles among the 50 companies tracked in Evident’s AI Index, 40% were focused on implementation. That compares to 31% that were focused on data engineering, 26% dedicated to AI development and 3% focused on model risk.
Recruiters say the shift toward implementation aligns with broader trends across the financial industry. Instead of hiring AI “builders,” financial firms are more focused on roles that have a more horizontal remit.
“Ten or 15 ago, you hired mostly technical people. They were looking for these capabilities, and were trying to build them out,” said Christoph Wollersheim, a consultant and co-lead of the AI practice at executive search firm Egon Zehnder. “What shifted is that you now need many more of what we call AI transformers, because they can actually create some impact through the scale up of the use cases.”
The challenge with finding “AI transformers” is the requirement to be knowledgeable about AI use cases, along with a detailed understanding of the financial industry.
“You’re now trying to find the person who’s in the financial services industry and knows everything about AI and, ideally, knows everything about the use case area that this person is supposed to be responsible for,” said Wollersheim. “If you are looking at a retail banking business unit, it’s getting very, very narrow now.”
Staffing for AI skills isn’t always about meeting a skills shortage, but part of the planning and investment in an organization’s future needs and priorities, said Jesse Skaff, executive director and head of the U.S. strategic account management team at recruiting firm Selby Jennings. He acknowledges the focus on implementation rather than building among financial firms’ AI priorities.
“I don’t think banks are necessarily looking for people who can invent or create from scratch,” he said. “Rather, banks are looking for those individuals who can spot use cases and run with use case ideas for implementation of AI.”
A key hurdle, recruiters say, is finding the right talent in the right geography, and meeting the high compensation requirements.
“Associate-mid level candidates working in machine learning and AI are earning as much as VPs who are working in more traditional tech roles,” said Connor O’Sullivan, principal consultant for financial technology recruitment at Selby Jennings. “I recently spoke with a candidate at a global financial services company with four years of experience in AI and machine learning, and he is targeting $300,000 [per year]. I also spoke with a traditional engineer at that same company who is [at the] principal level with 12 years of experience, and he has the same compensation ask.”
