Capital One Empowers ML Engineers to Expand Their Skills

With support from leadership and access to various learning resources, engineers like Fawaz Moshin are driving innovation through advanced machine learning models.

Written by Brigid Hogan
Published on Oct. 18, 2024
Photo: Shutterstock
Photo: Shutterstock
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Working at Capital One means joining a company that prioritizes continuous learning and professional growth, according to machine learning engineer Fawaz Moshin. For Moshin, joining the team has allowed him to use his existing knowledge to thrive while expanding his expertise into new areas of interest.

“Capital One’s culture and leaders are very supportive of us taking time to learn, and we have both internal learning platforms and external resources we can use,” he said. This environment encourages associates to stay up to date with the latest advancements in technology, providing the resources needed to deepen their knowledge and enhance their skills.

This support is particularly valuable when diving into complex ML concepts, as Moshin has experienced firsthand.

“I recently wanted to learn more deeply about a certain type of neural network called long short-term memory, which is often used for natural language processing,” he explained. “Through a mix of Udemy courses, access to textbooks and one-on-ones with colleagues, I was able to use these LSTM networks to build a new series of models that support an anomaly-detection product.”

Moshin’s work on LSTM networks illustrates how Capital One’s commitment to learning translates into innovative outcomes, showcasing the direct impact that continuous learning can have on solving real-world problems. By fostering a learning environment and providing ample resources, Capital One empowers its ML engineers to push the boundaries of their knowledge and deliver tangible results.

Built In Boston spoke with Moshin to learn more about how ML engineers thrive at Capital One.


 

Fawaz Moshin
Machine Learning Engineer • Capital One

Capital One is a financial services company that offers a wide range of banking products, including credit cards, savings accounts and loans, with a strong focus on leveraging technology and data to enhance customer experiences.

 

Describe a typical day with Capital One. What sorts of problems are you working on? What tools or methodologies do you employ to do your job?

I’m a ML engineer for the enterprise platforms and technology team. Our primary focus is solving complex ML problems that help our colleagues, who in turn assist Capital One customers in becoming more financially empowered.

We help teams solve problems using models we create from Capital One’s existing store of datasets. Our work can range from developing predictive time series models to implementing natural language processing. For me, most of this work happens using Python for data processing, internal platforms for orchestration of our applications and agile methodologies to help structure all of our work.

 

“Our work can range from developing predictive time series models to implementing natural language processing.”

 

Share a project you’ve worked on that you’re particularly proud of. 

One of the projects I’m proud of is helping to build a suite of anomaly detection and monitoring tools that help us identify suspicious or anomalous activity involving financial transactions.

Product managers, data scientists and ML engineers came together to build models that better allow us to service our customers, such as more quickly mitigating fraudulent activity.

 

Responses have been edited for length and clarity. Images courtesy of Shutterstock and Capital One.