Becoming a Force for Good: Daniel's Story

For programmers and engineers, the classroom is a great place to learn the hard skills like programming languages. But when it comes to soft skills—like working with non-technical people—the narrow confines of a technical major can sometimes get in the way of a student's development.

In early 2021, that was a concern for Daniel Monteiro, a software engineer in JPMorgan Chase's Software Engineering Program (SEP), a two-year training program that helps young engineers navigate the transition from the classroom to the office. Not long after starting with the bank, he realized if he wanted to move ahead in his career, he needed to pair his mastery of hard skills with a deeper understanding of the soft ones. At the same time, he worried he might not have the opportunity to continue building his hard skills—a vital concern if he hoped to remain at the cutting edge of his discipline.

Luckily, SEP had an option to help him build out both sides of his skill set: Force for Good.

Building Relationships

Force for Good is a JPMorgan Chase program pairing a team of 6-8 technologists with a non-profit organization for eight months. The team meets with the nonprofit, assesses its needs, and builds a technological solution it can use to advance its mission.

Force for Good isn't just a great opportunity to help an organization in need—it's also a chance for technologists to connect directly with their end users. With Force for Good, the technologists regularly talk to clients, learn about the client's needs, and work together to decide what to build.

“This experience helped me learn how to empathize with the users' needs, which is an important skill required to build great products," explains Monteiro.

Getting Real Feedback...And Experience

For Monteiro's first Force for Good project, he was paired with Rural Entrepreneurship and Livelihood Foundation (REAL), an Indian nonprofit focused on giving rural community members—particularly women—the training needed to start their own businesses.

REAL connects with its participants through online modules—a great tool for reaching participants in far-flung locations. Unfortunately, getting useful feedback about the training modules—a necessity if you plan to improve them, as REAL did—can be difficult. For example, students might not understand a lesson, may not be able to imagine ways to improve it, or may be introverts who are uncomfortable giving clear answers. REAL needed a way to get useful feedback, and Monteiro's team realized that machine learning could be the perfect tool to provide it.

Machine learning is a type of artificial intelligence in which the computer analyzes data, identifies patterns, and makes educated assumptions based on that information. In essence, it "learns" about something and then acts, with little human interaction. Spam filters are an example of machine learning that affects millions of users every day.

Unfortunately, designing and creating a machine learning tool takes time, money and expertise—and, like many non-profits, REAL had limited time and resources to apply to the problem. That's where Force for Good came in. By leveraging machine learning, the team was able to create an emotion recognition system that could get useful feedback from users, even when they were unable to provide it.

“It studies the learner's facial expressions every frame," says Monteiro. "While the camera is on, their emotions are tracked on a frame by frame basis, and we learn if they're happy, sad, confused, or surprised."

Obviously, the participants have to consent to being watched, and have to turn their camera on to participate. At the end of a session, the machine learning feedback is sent to the trainer to see how the participant's emotions changed throughout the session, particularly if they became distracted at any point.

With a better understanding of where students lose focus or misunderstand the subject matter, teachers can modify the way they provide the material. REAL can rewrite lessons to improve student comprehension, which enables the nonprofit to help more people in a shorter period of time, and enact more change in the world.

REAL's in-house tech team is still working on launching the overall training platform that will eventually feature the facial recognition component, so Monteiro hasn't seen the results of his work in the field, but he's excited about the potential. “To help more people is just awesome," he says. “I'm excited to see how it's going to play out."

Learning and Growing

Technology is constantly changing, so technologists' skills need to constantly evolve. Prior to this project, Monteiro hadn't worked with machine learning at JPMorgan Chase, although he knows it's a skill that's in high demand, and will help keep him on the cutting edge.

In terms of soft skills, he's made friends within the program and interacted with people he never would have met otherwise. “It's quite fulfilling, especially given that I could do it as part of my work," he says. “I always wanted to make sure that I could create a social impact in the world. Force for Good allowed me to do my bit in helping people who are disadvantaged and helping really great nonprofit organizations like REAL work." He's looking forward to working on another Force for Good program in the future.

Monteiro's interest in improving the world has continued, even after his project ended. "We spend loads of time at work, generally oblivious to so many issues present in the world today," he says. "Force for Good helped me learn a lot more about what's going on in the world, and made me want to figure out ways I can help." After his project ended, he volunteered as an English teacher with Teach for India, where he taught 15 year-olds.

Between his developing technical know-how and his new outreach into the community, Monteiro is well on his way to becoming his own force for good.