When did you start working at Pacific Life?

I began as a data science intern at Pacific Life in 2021. My internship lasted 12 weeks, but my manager extended that by four months, and I continued to work for Pacific Life while finishing my degree. My internship experience showed me that Pacific Life was a positive environment where I could grow my career. Later, I was offered a full-time position while still in graduate school which I accepted because I genuinely enjoyed my experience working with my fellow data scientists, leaders, analytics engineers, visualization team, and business partners.

How does Pacific Life support your career development?

Pacific Life prioritizes individual growth and fosters a positive, people-centric environment. It has been a great place to develop my skills with the support of my colleagues. As I’ve taken on more important and challenging projects, I’ve found the right balance between comfort and discomfort, knowing my teammates have my back.

My manager, Imelda, is excellent. Not only does she have a background as a data scientist, but she also held various technology-centric roles at Pacific Life before that. Her wealth of business knowledge makes her a one-stop shop for insights. On the flip side, she’s equally skilled in the technical aspects of data science. For our team, it’s about striking the right balance—combining technical expertise with business acumen to create valuable solutions. Learning from her extensive business knowledge has been invaluable. Imelda encourages her team to develop and continue to learn new skills —a crucial aspect of being a data scientist.

With the support of my leaders, I’ve decided to continue my education and pursue a second master’s degree though a part-time online program.

What are you working on lately?

Currently, I’m contributing to a dashboard that helps provide a list of financial professionals for Internal Wholesalers and Sales Associates to call. As a Data Scientist, my main role is to help build a predictive model to surface the highest quality financial professionals. I’m also working on ML Ops, or Machine Learning Operations, which is the practice of managing and automating the lifecycle of machine learning models. It’s important because it ensures models are efficiently deployed and maintained, making them reliable and useful for data scientists and stakeholders.

What are your career goals?

My current goal is to continue advancing in data science. I aim to reach levels where we take on increasingly important and high-value projects, utilizing data to transform our capabilities across our division. This goal has been with me since my internship days—it’s deeply ingrained in our culture and a driving force behind our internship program. I’m committed to pushing the boundaries of what’s achievable internally to enhance our competitiveness and decision-making.

What advice would you give to early professionals starting their careers?

When looking for a job, it’s important to remember that interviews are a two-way street—you’re evaluating the company just as much as they’re evaluating you. Ask questions to determine if the company is a good fit for you and if they are willing to invest in your growth.