Blog post by ELP Fellow Mark Jin (Computer Science | Class of 2019)
I knew there would be a huge gap between working at a startup and learning at school. I thought I could change smoothly between the two statuses since I’ve always been involved in student activities that could somehow mimic working at a company. I believed I was fully prepared since the Entrepreneurs Leadership Program had equipped us with the vision and methodology to be an entrepreneur.
But that was all what I thought. Life is always the best teacher. After my first two weeks’ internship as a data scientist at a startup focusing on offering value-oriented strategies for traditional industries to transform to Industry 4.0, I’ve come up with some thoughts on what I’ve learned at a startup that I didn’t at school, and hopefully this article will provide some food for thought for young entrepreneurs who want to experience a smoother transformation from a college student to a genuine entrepreneur.
Art of Compromise
I could tell that my arms were shivering when I stepped into my company on the first day. The reason is simple: This is the first time that I transformed my status from a student to an employee, so I turned to my mentor, who has just completed the transform from a student to CTO.
‘It’s an art of compromise.’
There are fewer constraints in college education, which cultivates a wonderful atmosphere for idealism, more specifically, academical idealism. But when it comes to work, more parties are involved in this game. For example, the colleagues from the same department who are working with your, colleagues from other departments, your customers and of course, your boss. Given such circumstance, the academical idealism becomes an obstacle towards efficiency. “We need to move forward and should eliminate the periods when we are totally stuck.”, said my mentor. The balance between excellence and efficiency requires all the parties to step backwards a little bit and find the common ground.
It’s never too old to learn
“I think I’m possessed by the hustle and bustle in this startup. And this is really dangerous.” This is the response I got when chatting with a colleague who joined the company one year ago. “So, what’s your biggest gain from such hustle and bustle?”
He got lost in thought. The complete one minutes’ silence reminds me of an old saying, the eight hours apart from your sleep and work everyday defines who you are in the future. When we are in college, our academic plan enforces us to take courses that not only focuses on the depth of our major, but also intellectual breadth. But sadly, typical companies only require their employees to complete the work assigned according to their skills. Some of my colleagues wrongly adopted the concept of “art of compromise” towards such situation and got lost in the hustle and bustle. Their life goal has been limited to finishing the work assigned.
The colleague who got lost in silence is a software engineer, who’s been planning to get transferred to business or management in his thirties. Sometimes he doubts himself whether he could achieve this goal. Hopefully, the conversation with him could act as a stimulus for him to implement the saying “it’s never too old to learn”. Some of my wise colleagues enforced themselves to learn beyond. Sometimes, I observe that their eyes are blinking with eagerness to learn.
Engineer? Not enough.
Thanks to the cultivation of College of Engineering, I start to take on my pride as an engineer. After two weeks of internship, however, I start to think about how to keep a harmonious relationship with such engineer pride. I can still recall the Wows I received when I solve a really tough engineering problem, but this pride seems unstainable in the industry.
“Hey Mark, the moment you choose internship in the industry rather than doing research at school has redefined your role as an engineer. After all, a good data scientist should have product mindset.” At first, to be honest, I’m really confused about such expression. In my mind, the top data scientists are those who can analyze data using the most advanced and complicated method in the shortest period of time. But time is the best proof that I’m wrong. During my first two weeks, a lot of effort is put on communicating with our customers, trying to persuade them that our way of data analysis works. Furthermore, the methods used are not that advanced and complicated, instead, they are quite simple and straightforward.
“Sometimes you will find that the simplest linear regression is the most useful method in industry”, said another data scientist in my company, “How can we tell a best data scientist from good data scientists? Mark, you yourself can tell it when they are presenting their thoughts to their customers.”
Customer and product oriented. I’ve heard this phrase a hundred times during my ELP training. But when it comes to my internship, these two phrases seem to automatically disappear in my mindset. The coolest engineer doesn’t solve the hardest problems. Rather, their solutions cater to their customers’ needs.
To learn how to learn is what to learn
A PhD who just got graduated started his journey in our company one week after me. The momentum of years of study gives me the incentive to pursue further education. So, a chat with this PhD about his biggest gain from graduate education follows as a matter of course. I expected he would give me answers like “My PhD degree prepares me really well to deal with industrial problems” or “What I’ve learned fit me well with this position.” Surprisingly, his answer is “Thanks to my PhD, I’ve learned how to learn. And that’s what I’ve learned during my PhD.”
To learn how to learn is what to learn. Actually, I have the same feeling as his even though I haven’t finished my undergraduate education yet. My internship position at my company is a data scientist. To be “qualified” to take this position, I took EECS445 Introduction to Machine Learning in my last semester. To be honest, the practicability of the specific methods learned during the course is too weak to apply to my internship projects, but both the high-level scope and structure on data analysis and the workflow of the course projects prepare me well to learn and work on real-world problems.
At that moment, I suddenly realized I’ve been making a big mistake in my past few years. Too much effort and emphasis are laid on getting a good grade rather than thinking one step further from what we’ve learned in class. I cannot deny the importance of the knowledge itself during such courses, but how we obtain such knowledge and why seems more important. Fortunately, we are always learning what to learn at school and what we need to do is just to step one step further. That is, to reflect on how we learn those what we learn. This might be the knowledge we should obtain at school.
Someone has told me that life is the best teacher. Now I will certainly convey this message to all my fellows. I’ve never experienced such eagerness to learn at school. Life is the best school. But it is not a traditional one. There is no traditional teacher, but everyone, every experience has something to teach. There is no exam or grading policy here, but interestingly there are hidden grading policies almost everywhere. There are no absolute top students here, but we should know what we want to be in five years, in ten years and further. The only competitor in this school is ourselves. Hopefully, we will all get a good “score” in this life-long and endless school.