The finance industry is banking on AI — and they're creating new jobs to bridge the gap.
Traditional financial institutions and fintech start-ups alike are looking for more candidates who specialize in artificial intelligence, machine learning and data science. According to reporting by Bloomberg reporting and data from LinkedIn, job listings requiring these skills in the financial industry increased nearly 60% in the past year.
According to Glassdoor data, "some of the most common job openings in AI and finance are for machine learning engineers and data engineers, among other highly specialized software engineering roles," Glassdoor senior economist Daniel Zhao tells CNBC Make It. "We're also seeing job openings for workers who can help navigate the AI landscape, including consultants and researchers. As companies establish the foundations for their AI functions, we're seeing employers hire more senior candidates to lead these new teams."
Not all new job functions are rooted in computer science or engineering, however. For example, chatbot copywriters (those who write conversational answers to technical questions customers ask on websites' "chat" functions), product strategists and technical sales representatives are also in demand, Zhao says. Those who have a business or communications background may be better suited to these roles.
And workers who already work in finance but are willing to learn more about AI have a leg up, Zhao says. "Their domain expertise in business and finance is a great way to differentiate themselves in a hot technical field."
Here's a look at some of Glassdoor's current postings of AI jobs in financial services, along with the job site's estimated salary range for each.
Senior Experience Designer, Bank of America
- Job description: Work on creating user-centered experiences for digital platforms, including mobile app, responsive web, ATM, artificial intelligence and other emerging technologies.
- Minimum qualifications: bachelor's or master's degree in design
- Estimated salary: $112,000 to $123,000
Data Scientist, Morgan Stanley
- Job description: Analyze data and develop predictive models for various use-cases within Sales & Marketing such as Sales Targeting & Segmentation, Lead Generation and Product Recommendation.
- Minimum qualifications: master's degree in computer science, statistics, applied math or relevant field
- Estimated salary: $127,000 to $184,000
Senior Product Manager of Commercial Credit, Capital One
- Job description: Lead teams of designers, engineers, data scientists and analysts to define product strategy and develop, launch, and enhance products and services. Apply technology like automation, machine learning and artificial intelligence and predictive analytics to re-imagine the way Capital One manages risk.
- Minimum qualifications: bachelor's degree in computer science or engineering
- Estimated salary: $65,000 to $112,000
AI Backend Engineer, J.P. Morgan
- Job description: Design and build core data backend systems and machine learning platforms to transform the operations of the business. Develop fault-tolerant, scalable backend systems that process data and serve machine learning requests.
- Minimum qualifications: bachelor's, master's or PhD in computer science or related quantitative field
- Estimated salary: $89,000 to $111,000
Learning AI without a computer science degree
Professionals with a background in engineering will have a growing field of opportunities within the finance space. For those without a STEM education, however, the ability to adapt and learn such skills will be crucial across a wide set of job functions. "With numerous online courses and boot camps available, it's never been easier to learn AI and machine learning skills that can enhance your career," Zhao says.
LinkedIn provides online courses to learn skills like cloud computing, artificial intelligence and analytical reasoning. Hundreds of universities around the world offer online courses for free — or partially free — with many falling in the categories of computer science, mathematics, programming and data science. Furthermore, training academies and boot camps have cropped up in order to bridge the gap of working professionals who want to pick up technical skills that can translate to a new role or enhance their current work.
The question of whether workers will have to seek out these opportunities, or if they'll be encouraged and provided by employers, hangs in the balance.
"It's important for companies to continue to invest in their people so that they are up-skilling and re-skilling their people to keep up with the roles that are in demand," said Feon Ang, vice president for talent and learning solutions in Asia Pacific at LinkedIn, to CNBC's "Capital Connection." "At the same time, people need to continue to invest in themselves and have a growth mindset."
A recent report from IBM suggests employers recognize the increasing need to retrain workers — an estimated 120 million worldwide within the next three years — as a result of AI and automation. However, executives from the report point to soft skills such as flexibility, time management, and ability to work on teams as skills more important than technical STEM knowledge or basic computer and software/application skills.
Hiring adaptable professionals and investing in training programs in data science, engineering and AI can help businesses drive technological innovations from within, the IBM report suggests.
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