How Mentoring Can Help You Land a Data Science Job
Students at NYC Data Science Academy have the opportunity to be teaching assistants (TAs) after graduation. For Tristan Dresbach, a recent alum, working as a TA inspired him to continue mentoring online bootcamp students outside of his work as a data scientist at Unilever. He graduated from the in-person immersive data science bootcamp in December 2018 and soon started working with Unilever on a truck and load network pooling optimization algorithm. We asked him about his experience of being a TA in the hopes that it will help future students decide if it is the right choice for them.
Most students know what a TA is. You’ve probably had several TAs over the course of your education. For those who aren’t familiar with the role, however, here is a brief list of responsibilities that bootcamp TAs have:
- Lead homework sessions about anything from writing a for-loop in Python to choosing hyperparameters for a random forest model
- Help students with ad-hoc questions about coding, machine learning concepts, and projects
- Create exercises for students
- Provide regular feedback to students
Tristan told us, “Being a TA has been one of the most rewarding experiences in my professional career so far. The main reason I absolutely loved being a TA, and why I became an online mentor after, is because of how much I enjoy teaching. It is tough to put into words how rewarding it is to guide students through the learning process and watch them grow as they hone their skills and obtain employment offers. It is a humbling experience to be able to help students, both technically and emotionally, through the 12-week bootcamp. I enjoyed being a TA so much that I spent most of my weekends on Slack and in Google Hangouts sessions helping students with their projects.”
In addition, Tristan explained that being a TA can benefit you in your job search. Answering students’ questions force you to hone your technical skills and knowledge of the bootcamp material. He says “force,” because students will keep asking questions until they are satisfied with your answer, pushing you to solidify your understanding of concepts.
Tristan said that this was “tremendously helpful” in his interview process. “For example,” he explained, “during a phone interview I was asked 20 or so rapid-fire Python questions on the spot (i.e. how would you find columns that contain NA values, how would you subset a data frame for a specific month, how would you add enough leading zeros necessary so that every number in a column is 10 digits). To my surprise, not only did I enjoy answering these questions, but I was able to answer every single one of them in quick succession. I was prepared for that interview thanks to constantly answering students’ questions about how to build or fix things, and how to make the code more efficient.”
You can also leverage your position as a TA in negotiations with employers. Most of the time, employers are impressed when they see that a student has a TA or mentoring experience. Being a TA shows that you have strong technical skills and an aptitude for communicating complicated concepts to people with varying degrees of technical knowledge. This is an invaluable skill for data scientists who are frequently required to explain things to project managers, upper management, and other non-technical co-workers. “As evidence of how much employers value this skill,” Tristan told us, “during behavioral interviews, three different firms asked me to explain in plain English how logistic regression, penalized regression, and multiple linear regression works, respectively.”
We hope that Tristan’s reflections will help you make a well-thought-out decision about whether the position is right for you. If you have any questions about being a TA or student, the transition to becoming a data scientist, or anything else, please do not hesitate to contact us at [email protected]. Our next immersive data science bootcamp starts on July 1, 2019.