Recently, Christopher, Managing Partner at Tri5 Ventures, reached out for an interview about “The Life of a Data Scientist”. The intent is to share knowledge and insight with people aspiring to enter the field, or those currently practicing data science.
The article was published a week ago on Tech in Asia and can be found here: “4 Singapore-based data scientists share how data has been impacting lives”. It covers data science professionals across multiple backgrounds, including researchers, entrepreneurs, and startups.
A few people have asked if I could build on what was shared in the article, so I’m reproducing my complete responses to Christopher here.
TL; DR: My interest in understanding human perception and behaviour led me to pursue psychology, statistics, and machine learning. With these skills, I joined Lazada’s Data Science team in 2015.
Wow, I can’t remember the last time being asked this.
Since my undergrad days (when I was pursuing psychology and statistics), I’ve been interested in how people perceive, think, and behave. This required measuring perception (surveys, etc), behaviour (actions, etc), and outcomes, and making inference. In other words, data and statistics.
I then got an opportunity to apply this in IBM’s Workforce Analytics team, where we built an internal job recommendation engine for employees and tracked outcomes. The goal was to help people level up in their careers, improve job engagement and performance, and reduce attrition.
Along the way, I gained an interest and picked up machine learning. Statistics helps us to understand historical data and make inferences; Machine learning helps us learn from historical data and make predictions.
I then got an opportunity to join Lazada’s data science team in 2015, thanks to Kai Xin and John. I’m not exactly sure when my career in data science started, but I guess joining Lazada Data Science might have been it.
Myth: Machine learning is 80% of the work (is this a cliché myth?)
Truth: Machine learning is about 20%.
Note: The above breakdown is unique to each data scientist, but it’s roughly how my time is spent.
Myth: I (only) need to be a technical rockstar.
Truth: Being a technical rockstar helps, especially with what I’ve listed above—but it’s not enough to really make an impact. You also need to be able to:
A simple chart on how my time is spent here.
Within Lazada, data science has helped in several ways, including:
In my free time, I’ve been dabbling with using deep learning to:
The possibilities seem limitless, and there’s lots opportunity to use data to create positive impact and improve lives.
This is a pretty broad question.
If you’re asking about how my time is spent, here’s a chart I created a while back.
If you’re asking about life satisfaction, I’m pretty satisfied. My mission is to use data to create positive impact and improve lives—being a data scientist allows me to achieve this.
Has my perception changed? Yes I guess.
Sometimes, I can’t help but see things in our natural world and wonder how to measure them and use data to improve outcomes.
I’m also a bit more skeptical about claims people make, and tend to be conservative and cautious when there’s no data backing the claim.
I’ve received this question numerous times—enough for me to document it here on my site. It covers some tools and skills, where to learn them, and how to practice them.
Questions? Want to follow my journey? Reach out on Twitter @eugeneyan!
I write about how to be effective in data science, learning, and career. Get weekly updates.
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