From Education to Real-Life Inspiration: Springboard Alum Haotian Wu Shares His Story


Here at Springboard, not solely will we pleasure ourselves on our college students’ successes, however we genuinely imagine that their goals are what make up the inspiration of our mission. Our alumni dream huge – they usually make huge strikes in stride.

So, in our collection of Student Spotlights, we’re shining a light-weight onto a few of our favourite alumni tales: their journeys inform tales of accomplishment, grit, and dedication in opposition to every kind of odds.

In this Student Spotlight, we introduce Haotian Wu: a Product Manager and Analyst who needed to go deeper with information to harness the ability of Machine Learning and NLP (pure language processing). Learn extra about his journey beneath.

A bit bit about Haotian…

Haotian graduated from the Shenyang Institute of Aeronautical Engineering with a level in Mechanical Engineering in 1998, happening 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, started working as a Product Manager at Semanengine in Boston.

The tech scene was altering quickly at the moment – and the ability of data science was evolving with every day. Dedicated to scaling his information wrangling abilities, Haotian determined to be a part of Springboard in February 2019. Within 7 months, he accomplished the curriculum and commenced the job search part of the course. four months later, he landed a task as an Associate Data Scientist at Ascensus, the biggest impartial retirement and school financial savings companies supplier within the United States.

What initially you in data science?

In my earlier job, I discovered that Natural Language Processing methods had been always being developed. I needed to study extra about Data Science, Machine Learning, and Natural Language Processing in order that I might get extra insights from information and be immediately concerned in fixing extra issues.

Why did you select Springboard?

Between my Master’s, Ph.D., and MBA, I had carried out conventional academia and, in-class schooling for a very long time (possibly even a bit longer than most!). So once I determined I needed to go deeper into studying about data science, I knew that I used to be effectively outfitted with the technical information, however I wanted hands-on, real-life steering with issues like capstone undertaking ideation and understanding how data science got here to life within the enterprise setting.

Another enormous cause I selected Springboard was that I might pay the schooling after discovering a job, which was an enormous monetary reduction that permit me concentrate on my schooling in the mean time with out the monetary strain looming over my shoulders.

What was your Springboard expertise like?

I had a really constructive expertise, given the depth of the programs and the well-thought-out program construction. The programs had been fairly intensive. The capstone tasks had been mentally difficult, actually time-consuming, however how rewarding and enjoyable they had been made it value it! Plus, the Springboard mentorship workforce and community (each profession and data science-wise) had been actually useful. I actually appreciated their efforts and recommendation.

How did you benefit from the Springboard group?

I linked with different college students by way of our Slack channel and practiced the interview questions with different Springboarders a number of occasions all through the Career Track. I additionally linked with a number of Springboard alumni by way of LinkedIn. Serendipitously, my hiring supervisor can be a Springboard alumna from 2018!

What was your capstone undertaking?

Boston Fire Alarm Prediction: This undertaking analyzed whether or not a fireplace alarm was true or false within the Boston Area. False hearth alarms usually delay the operation of the Fire Department and trigger monetary loss. So, utilizing information, my capstone might assist the Boston Fire Department to establish false alarms extra successfully, which in flip would enhance effectivity. Working with information on real-world functions like this one is a wonderful means of seeing how information impacts our day-to-day lives.

What was essentially the most useful a part of your Springboard expertise?

My mentor’s steering enriched my studying by exhibiting me somebody’s perspective who had hands-on expertise with actual data science tasks — as somebody with a extra technical background, my mentor calls ended up being extra like discussions. I usually requested deeper questions in regards to the software of Data Science in enterprise. My mentor’s solutions had been extremely detailed and gave me the mathematical background on subjects that I discovered very useful. It’s additionally value noting that my profession advisor actually helped me within the job search.,

What recommendation do you might have for present Springboard college students (particularly these simply beginning out the job search part of their program)?

  1. Find your personal energy. Data Science is de facto broad and contains Analytics, Product, Algorithm, Data Engineering. Which half(s) are you good at?
  2. Network, community, community. Please proactively community, tapping into all channels of your community: Family/buddies, Alumni(from each step of your schooling), LinkedIn, Conference/Meetup/Community, possibly even strangers in bus/airplane/subway.
  3. Get extra suggestions from employers, particularly those that reject you. Then you’ll be able to study what you’ll be able to enhance and might modify your job search.
  4. Try your finest to perceive what the job actually does as a substitute of merely trying on the job descriptions. Sometimes, employers would possibly change the JD for you in the event that they really feel you’re the type of candidate they need at their firm.
  5. Be affected person and sort to your self. Job looking out may be very mentally difficult! 3-6 months or longer on job looking out is quite common, so keep in mind to keep assured in your self and keep in mind to be versatile.

Ready to begin or develop your data science profession? Check out our Data Science Career Track —you’ll study the talents and get the customized steering you want to land the job you need.


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