What Bank of America’s Chief Data Scientist Thinks About Getting a Master’s in the Field
THROUGH Lake SydneyJanuary 21, 2022, 2:46 PM
A Bank of America branch in New York, seen in January 2022. (Photographer: Victor J. Blue—Bloomberg/Getty Images)
Computers aren’t what they were in the 1980s. Take it from Dave Joffe, chief data scientist in Bank of America’s Comptrollership Technology Department. He saw how technology, data and computing grew over decades and eventually converged.
Joffe has been a data scientist for most of his career, although his roots are in another type of science. He earned an undergraduate degree in psychology before discovering a fascination with computers while working in a neurobiology lab. Joffe first learned to program on the lab’s microcomputer, “which started a whole path that I couldn’t have foreseen,” he says. Fortune.
After working on the microcomputer in the lab, Joffe decided he wanted more formal training and returned to get his master’s degree in computer science. This landed him a position as a programmer at Bank of America, where he gradually became a leader in data science over the years.
In 2020, there were approximately 2.7 million open data analytics or data science jobs and a 39% growth in employer demand for data scientists and engineers, according to IBM. Although the demand for these positions is high, large companies like Bank of America are always looking for high-quality candidates to fill open positions. In fact, Joffe recalled a year of searching for a candidate for a data science role at Bank of America.
What can aspiring data scientists do to prepare for the ever-changing world of data? Fortune recently sat down with Joffe to discuss important considerations for future data scientists.
The following interview has been edited for brevity and clarity.
Fortune: What were the options available to you when you resumed your master’s degree and why did you choose what you did? How is this different from today?
Joe: Yeah, very different. I just knew that I needed more formal training in what I was doing, which was programming. And computing seemed to be the closest to what was going on then, but it put me on a very particular track and I wouldn’t even have thought of the other track that was there then, but now they merge.
I could have taken a math, applied math, or statistics program — I could have gotten a master’s degree in math or statistics. At the time, there was no way for me to know that it was very much related to the programming that I was doing. I just couldn’t know.
Fortune: What is your advice to data science professionals today? Should they pursue a master’s degree?
Joe: Notwithstanding the cost of all these things—that’s a discussion on its own—they were much cheaper when I did this. Nevertheless, the answer is yes. This knowledge is important. Experience too, skills too. But they are all hybrids.
A graduate degree is very helpful in building a knowledge base. But it’s going to depend on which master’s degree and which hybrid – about four fields, areas or disciplines – you’re interested in.
One can have a master’s degree in computer science that still applies, especially in the field of database programming, because the nature of data science is that it is data-intensive. So you better be a very good programmer with very large amounts of data. Data science applies to everyone, but computer science would be great programming skills.
Second, the whole area I mentioned earlier: statistics and machine learning. It would be more, say, a master’s degree in math, applied math, physics, or some are even biology.
Third, what about business domain knowledge? Data science is an interesting term. It’s a term I don’t agree with in the sense that it’s really the science of something else whose data allows them to do science. It is not the data itself, the science of data information theory that is at issue.
In the case of Bank of America, it is the science of banking, which falls under economics and finance and the application of programming and skills in deterministic and probabilistic programming. The first two make this useful. So if you are on business domain knowledge and want to build a foundation in it, MBA with Business Analytics, Masters in Business Analytics all seem appropriate for this field.
Fourth, it’s the rarest of fields, we don’t find many people with hybrid skills here. It’s that these are such complex fields that the ability to communicate and visualize results to match these complex artifacts is probably best pursued by the hybrid of them all, which is a master’s in data science or an MBA with analytics .
Fortune: What does Bank of America look for in top data science candidates?
Joe: Sure, we’re looking for those degrees, but I’d say any undergraduate with a STEM background of various sorts coupled with machine learning experience – machine learning, I would estimate, is over 90% of all techniques used in data science. There is a revolution going on.
If anyone has a lot of machine learning experience… we like that too. We already know that we have to accept people who are going to be weaker in in-depth knowledge thanks to advanced degrees, and we train a lot in-house.
Fortune: What other continuing education opportunities should data scientists pursue?
Joe: I think in general, if you’re a data scientist at Bank of America, Bank of America has extremely valuable data and we appreciate that data. And so they can gain that experience. So it’s not just the training, it’s on the job. What I think is extremely important for anyone beyond certification and degrees is to gain skills, knowledge and experience and find valuable patterns in data. This is the key.
That’s how you gain value. It turns out that those who can find valuable patterns in data become valuable themselves. It does mean, however, that they want to be at a company that has valuable data, that is loaded with value so they can gain that experience.
Fortune: What other advice do you have for aspiring data scientists?
Joe: Lifelong learning is important for anyone who really wants to get into this field, because the nature of these fast-growing fields is that they evolve very quickly.
So lifelong learning in the areas that we’re in right now, which includes all the great work that’s going on in language processing, or unsupervised learning, or learning through reinforcement, or GANS (which stands for Generative Adversarial Networks), or Complex Systems, which have become very much in favor with climate modeling at the moment. Fairness testing, explainability, which goes hand-in-hand with the company-wide ESG focus on environmental, social and governance issues.
All of this is changing very quickly. It is not necessary to know them all, but it will be necessary to continually learn to keep pace with this evolution.
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