Here's How Bank of America Is Nurturing the Next Generation of Data Scientists
A principal element of data management is making sure you as an organization have the right people in place to make the most of the data flowing through it.
Computers and the information they contain are a far cry from the machines of yesteryear and they require a whole new set of skills to get the most out of them. This means, while still important, more general IT qualifications aren’t really cutting the mustard when it comes to instilling in people the skills they’ll need to become a modern data scientist.
As a huge innovator and investor in technology, Bank of America has been at the vanguard of this new movement for some time and has some sound advice for anyone looking to establish data science as their primary career path.
Bank of America
The Charlotte-headquartered multinational investment bank and financial services holding company is no stranger to technology. In fact, the company has recently set a new record for acquiring patents for technologies including AI and machine learning, blockchain, and data, having been granted 227 in the first half of 2021.
"We’re in a period of unprecedented change, and as any great company knows, delivering for customers and clients requires a strong focus on innovation," said Bank of America Chief Operations and Technology Officer, Cathy Bessant in a statement. "The culture we’ve created at Bank of America is immensely creative and forward-looking."
In 2020, there were approximately 2.7 million open data analyst or data science jobs available and a staggering 39% growth in demand from companies for data scientists and engineers. According to Glassdoor, at the beginning of 2021, data scientist was the 3rd best job in America with a median base salary of $107,801 and a job satisfaction score of four out of five.
However, there is still a massive skills gap when it comes to filling these roles and finding the right candidates can be a significant uphill battle, according to Bank of America. However, with the prevalence of specific data science master’s degrees now available, acquiring these skills is not out of reach.
"One can have a master’s in computer science is still applicable, especially in the area of database programming because the nature of data science is that it’s data hungry," said Chief Data Scientist at Bank of America’s Control Function Technology Department, Dave Joffe. "So, you better be a very good programmer with very large amounts data. Data science is applicable to all of them, but computer science would be very strong programming skills. Second is the whole area that I mentioned before: statistics and machine learning.
Data Science
When it comes to data science at Bank of America, it is looking primarily at the science of banking. This means an understanding of economics and finance and being able to apply this with deterministic and probabilistic programming skills.
People with an MBA with business analytics, or a master’s in business analytics all seem appropriate for that domain, but the area in which Bank of America struggles the most is with hybrid skilled people with a master’s in data science or an MBA with analytics, which has led to the financial giant being a little more flexible in the candidates in considers.
"Certainly, we do look for those degrees, but I’d say that any undergraduate with a STEM background of various sorts coupled with machine learning experience – machine learning I would estimate is greater than 90% of all of the techniques used in data science," continues Joffe. "There’s a revolution happening. If someone has a lot of machine learning experience, we like that, too. We already know that we need to accept folks who are going to be weaker in either deep knowledge through advanced degrees, and we internally train a lot."
Final Thoughts
It seems that, to fill the skills gaps in modern data scientist requirements, organizations would be wise to follow Bank of America’s example and adopt a more flexible approach when it comes to the qualifications you demand of candidates.
That’s not to say any business should settle for second best when it comes to the people you want facilitating your data management and analysis strategies, but rather think about where those skills might come from outside of specific qualifications – such as with other STEM degrees – and whether any gaps in the required knowledge or skills could be filling with additional and ongoing in-house training.
"Lifelong learning is important to anyone who actually wants to go into this field because the nature of these fast-growing areas is that they evolve very quickly," adds Joffe. "So lifelong learning in the areas that we’re in right now, which include all of the great work that’s going on in language processing, or unsupervised learning, or reinforcement learning, or GANS (which stands for generative adversarial networks), or complex systems, which have become very much in favor with climate modeling right now."