Advancing your career through machine learning

Teresa’s passion for all things tech was first sparked by her love of solving complex puzzles.

Throughout her 30 years of experience in the industry, she followed that passion to Capital One, where she grew her career from quality assurance analyst to her current role as senior software engineer. Along the way, she’s earned certifications on Amazon Web Services and data science to keep up-to-date with emerging technologies. The logical next step? Enrolling in MLETP to learn more about Capital One’s machine learning initiatives.

The program ended up teaching Teresa much more than just technical know-how.

"I love tech because I can lose myself in it,” Teresa said. “I lose track of time. Once I started with machine learning, the same thing happened. I found a new passion.”

MLETP offered Teresa hands-on experience to learn the entire life cycle of the machine learning process, from data analysis through implementation. She learned about the different ways Capital One uses machine learning, from fighting fraud to customer service.

MLETP also led Teresa to a new community that shared her passion for machine learning. Different Slack channels and options for both instructor-led and online learning fostered a welcoming environment. All learning styles were supported and anyone could ask and answer questions, from fellow students to top industry professionals.

“Capital One's culture of belonging is sincere,” Teresa said. “It's not just a tag that they say. They really implement it and you're always encouraged to disrupt, to talk, to give your opinion, to be a part of things, to value your perspective.”

As Capital One breaks new ground in the field of machine learning, associates like Teresa are able to take advantage of innovative tech to help advance their own career and find new passions. Additionally, Teresa’s involvement with Women in Tech Business Resource Group will also allow her to pass her new-found knowledge to the next generation of puzzle solvers.

“It’s critical to keep building your skills and keep learning what's happening in the future in technology,” Teresa said. “Everything changes so quickly, and you can do things so much more efficiently if you keep learning new things like machine learning. There's constant change and innovation.”