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Here at Springboard, not only do we pride ourselves on our students’ successes, but we genuinely believe that their dreams are what make up the foundation of our mission. Our alumni dream big – and they make big moves in stride.
So, in our series of Student Spotlights, we’re shining a light onto some of our favorite alumni stories: their journeys tell stories of accomplishment, grit, and determination against all kinds of odds.
In this Student Spotlight, we introduce Haotian Wu: a Product Manager and Analyst who wanted to go deeper with data to harness the power of Machine Learning and NLP (natural language processing). Learn more about his journey below.
Haotian graduated from the Shenyang Institute of Aeronautical Engineering with a degree in Mechanical Engineering in 1998, going on to earn his Master’s in Mechanical Engineering (‘03) and Ph.D. in Philosophy from the Northwestern Polytechnic Institute in China (‘08). Haotian moved to the United States in 2014 to get his MBA from Babson College, and after graduating, began working as a Product Manager at Semanengine in Boston.
The tech scene was changing rapidly at this time – and the power of data science was evolving with each day. Dedicated to scaling his data wrangling skills, Haotian decided to join Springboard in February 2019. Within 7 months, he completed the curriculum and began the job search phase of the course. 4 months later, he landed a role as an Associate Data Scientist at Ascensus, the largest independent retirement and college savings services provider in the United States.
In my previous job, I found that Natural Language Processing techniques were constantly being developed. I wanted to learn more about Data Science, Machine Learning, and Natural Language Processing so that I could get more insights from data and be directly involved in solving more problems.
Between my Master’s, Ph.D., and MBA, I had done traditional academia and, in-class education for a long time (maybe even a bit longer than most!). So when I decided I wanted to go deeper into learning about data science, I knew that I was well equipped with the technical knowledge, but I needed hands-on, real-life guidance with things like capstone project ideation and understanding how data science came to life in the business setting.
Another huge reason I chose Springboard was that I could pay the tuition after finding a job, which was a big financial relief that let me focus on my education at the moment without the financial pressure looming over my shoulders.
I had a very positive experience, given the intensity of the courses and the well-thought-out program structure. The courses were pretty intensive. The capstone projects were mentally challenging, certainly time-consuming, but how rewarding and fun they were made it worth it! Plus, the Springboard mentorship team and network (both career and data science-wise) were really helpful. I really appreciated their efforts and advice.
I connected with other students through our Slack channel and practiced the interview questions with other Springboarders a few times throughout the Career Track. I also connected with a few Springboard alumni through LinkedIn. Serendipitously, my hiring manager is also a Springboard alumna from 2018!
Boston Fire Alarm Prediction: This project analyzed whether a fire alarm was true or false in the Boston Area. False fire alarms often delay the operation of the Fire Department and cause financial loss. So, using data, my capstone could help the Boston Fire Department to identify false alarms more effectively, which in turn would improve efficiency. Working with data on real-world applications like this one is an excellent way of seeing how data impacts our day-to-day lives.
My mentor’s guidance enriched my learning by showing me someone’s perspective who had hands-on experience with real data science projects — as someone with a more technical background, my mentor calls ended up being more like discussions. I often asked deeper questions about the application of Data Science in business. My mentor’s answers were incredibly detailed and gave me the mathematical background on topics that I found very helpful. It’s also worth noting that my career advisor really helped me in the job search.
Find your own strength. Data Science is really broad and includes Analytics, Product, Algorithm, Data Engineering. Which part(s) are you good at?
Network, network, network. Please proactively network, tapping into all channels of your network: Family/friends, Alumni(from every step of your education), LinkedIn, Conference/Meetup/Community, maybe even strangers in bus/plane/subway.
Get more feedback from employers, especially those who reject you. Then you can learn what you can improve and can adjust your job search.
Try your best to understand what the job really does instead of simply looking at the job descriptions. Sometimes, employers might change the JD for you if they feel you are the kind of candidate they want at their company.
Be patient and kind to yourself. Job searching is very mentally challenging! 3-6 months or longer on job searching is very common, so remember to stay confident in yourself and remember to be flexible.
Ready to start or grow your data science career? Check out our Data Science Career Track —you’ll learn the skills and get the personalized guidance you need to land the job you want.