It's hard to believe that 2020 is really coming to an end. It's been an eventful year globally, but rather peaceful in my own little world.
I was hibernating for the past few months, and broke my word to keep the blog live. Part of it was the result of lacking inspiration, as well as not having enough readings. To me, writing has a purpose, which is about conveying ideas and thoughts to readers in a simple and clear manner.
I've always wanted to write down my journey of transitioning from finance to data, but have been hesitant till lately. This post has nothing about becoming a data scientist without a PhD or STEM degree. It also shares nothing on online resources for learning Python or machine learning models. While the journey is rather personal, I'd like to share my decision making process, the struggles I faced, and how I embraced changes and discomfort.
Why I made the move#
I was lucky to land my very first job in investment banking. It was and it is still considered a very prestigious job in the industry, and I was proud to have worked with so many smart and ambitious people. The banking experience had definitely opened my eyes as a grad straight out of university, and some of my colleagues there have helped shape who I am professionally.
However, I also knew that it wasn't for me in the long term.
It's common for junior bankers to switch after working for a few years. Some may go to business school, others may exit to private equity or hedge funds. There are also those who choose to stay and progress further.
I somehow got interested in the fast-growing tech industry after working on a few deals with top Chinese tech firms like Alibaba, Xiaomi and Tencent. I really wanted to be involved but was totally unsure what I could do in a tech firm without any programming skills.
It was also in banking that I found out about companies like Airbnb and Stitch Fix. I spent time doing my research on those companies and reading their blogs (here and here). This was how I found out about data science, 5 years after this famous article: Data Scientist: The Sexiest Job of the 21st Century - was published on Harvard Business Review.
Through these blog posts I became fascinated by the context of the job - digging through massive amounts of data, building models and making predictions to pull out insights, driving decision making and improving product features. It felt like a job that would be intellectually stimulating and rewarding.
I then started reaching out to people in the Bay Area who have been working in tech to understand more about data science. I also used LinkedIn to connect with people who made the transition from similar backgrounds.
It didn't seem impossible for me to start from zero. After all, the longer you wait, the higher the stakes.
In mid 2017, I came back to Sydney without a backup plan. As someone without a quantitative background, I decided to enrol full time in a Master of Statistics.
The first semester was hard - the coursework was heavily theoretical and I wasn't really blending with university life anymore. I was also living on my savings, and watching my balance going down everyday made me anxious. Despite trying very hard to convince my parents of my decision, they sometimes also had doubts.
All of these made me wonder if I had underestimated the difficulty of transitioning from one industry to another, and had been overly confident about myself.
After one semester, I started looking for data-related jobs with a plan to study part-time instead, as I found myself learning the most by doing. However, here in Australia, local businesses value local experience more. As I switched from a completely different industry, my previous experience was totally overlooked.
It sounds like a cliché, but fortunately there was light at the end of the tunnel.
One day, I got a message about a part-time statistical researcher role in the university, and shortly after was headhunted to join the data team for a local airline's frequent flyer program. It wasn't anything like what I had planned initially (to join a tech company); however, it was at least a good step forward.
Student of life#
This year, I celebrated my 2nd anniversary at Canva. Looking back, I found myself extremely lucky - I basically gained everything I know about data at work, and learned from some of the brightest people in Australia.
Here are a few things I've learned so far:
- Data itself doesn't tell a story; people do
- Technical skillset is a stepping stone, but not everything
- A large part of the job is about collecting and cleaning data to make it useful and accessible
- Another important element of the job is to communicate data findings in the simplest form that receives a stakeholder's buy-in
- Productionising an ML model is crucial and involves heavy engineering, but is rarely brought up
- Ad hoc analysis is common; interesting and impactful projects are rare
- Staying adaptive and curious can take you much further
I'd like to emphasise the very last point specifically as it strongly resonates with the phrase “Stay Hungry, Stay Foolish” by Steve Jobs. This has been the inner force driving me to keep learning and absorbing new knowledge. There's still so much that I don't know, not only about work, but also about life.
The other day, I mentioned that in the upcoming years, I want to double down on what I enjoy doing. It's hard to manage all the externalities, but at least I can try to control what's within my realm.
So in 2021, it's about being content but not being complacent; it's about being present but dreaming wild; it's about staying curious and embracing changes. I am and always will be a student of life.
I have so much hope and I hope you do too. ✨