As an analyst for the Federal Energy Regulatory Commission, Aaron Pujanandez focused on market power studies of the U.S. electric industry. He worked with data, but on the level of making recommendations on future events based on assumptions that they would match historical trends. For Aaron, that wasn’t satisfying. Aaron’s interest in more advanced data science topics grew from his frustration with the limitations of the data tools that were available to him. He decided to explore new techniques so he could develop better answers to the questions he was struggling with.
When researching education options, Springboard’s Data Science Career Track stood out to Aaron for several reasons: He wanted something online and self-paced since he was a new dad with a full-time job. But the biggest draw was the one-on-one mentorship. Prior to Springboard, Aaron had a professional mentor who answered work-specific questions, but he was looking forward to working with someone who could make a real impact on his future career and learning goals.
Throughout the entire course, Aaron always had the support of his mentor, Hobson. But the value of mentorship really became clear while Aaron was working on his capstone projects.
For Aaron’s first project, he chose a business problem that was directly applicable to his job, but it wasn’t really a data science question. The project was, Aaron said, “a mess.” But it was a blessing in disguise. Aaron’s mentor jumped in and turned the project into a learning experience, helping Aaron think through the process, better understanding the project, the blockers, and potential solutions. As a result, Aaron completed his first capstone with a deeper understanding of the data science process.
“The first capstone project was a mess. I had picked it with an incomplete idea of what data science was and how to best go about planning a project. My mentor was excellent in helping me realize this early and reshape my project into something more suitable.” Going into his second capstone project and with the help of his mentor, Aaron confidently selected a Kaggle competition. Armed with clean data, a clearly defined objective, and lessons learned from his first project, Aaron had a much more enjoyable time: “I was like, ‘Oh yeah, this is all easy. I can do this because I had already gone through failing multiple times with the last one.’”
There are many different types of students in the Data Science Career Track: upskillers, career switchers, and those who just want to learn data science. Aaron’s goal was to enhance his current analytical skill set. And he did just that.
Aaron worked with his career coach to craft a pitch for creating a brand new position for himself at the Federal Energy Regulatory Commission. To do this, he used something he learned from the Springboard course. To demonstrate the need for the position he was pitching, Aaron turned the issue into a classification problem and built a model around the issue, utilizing natural language processing and prediction models. Aaron used his new skills to demonstrate an issue in a way no one else at the organization had.
The result: Aaron became the Federal Energy Regulatory Commission’s data science advisor. After gaining so much from his time with Hobson, Aaron got to become a mentor of sorts, as part of his work involved teaching machine learning courses to colleagues.
Armed with the skills Aaron learned through Springboard’s course and his experience in his newly created role, he recently moved on to Accenture Federal Services, where he’s an analytics and modeling manager. “In my new position, I’m able to use my data science skills to help government agencies derive insights from their data to drive business decisions. As a manager, I’m able to work more as a mentor and advisor for junior data scientists just starting their career.”
Want to take charge of your career and switch to a data science role? Apply to the self-paced, mentor-guided Data Science Career Track today!