Grace Tang was born and raised in Singapore. She then went to the US for college and graduate school. She obtained her PhD in neuroscience from Stanford University, working as part of the Decision Neuroscience Lab to study – how personality traits, emotions, and external stimuli affect decision-making.
“While I was in grad school in Silicon Valley, I became infected by the tech bug and learned how to program. I also picked up machine learning for my thesis project,” says Grace who is a data scientist at Uber, carrying out behavioral science research for the Asia-Pacific region. She is the first international member of Uber Labs. She was formerly lead data scientist at online real estate platform 99.co.
Describing her role at Uber, she says, “I am based in Singapore, where I help various teams in the Asia Pacific region design, conduct, and analyze experiments. I also build internal tools which allow teams to run analyses on their own.”
How Uber uses Data Science
”Like in many other companies, there’s a wide variety of data science roles at Uber. Some data scientists build predictive models, others focus on conducting analyses or experiments to inform decisions with data, and others prototype and develop services and tools.”, explains Grace.
Talking about the current trends in data science industry, Grace says, “The amount and variety of data being collected is growing exponentially, and the number of innovative ways to use this raw data is also increasing. I feel that the demand for data scientists will grow in tandem.”
When asked about the most challenging part of her job, she says, “One of the biggest challenges I face is communicating the fact that a “failed” experiment (one where a hypothesis wasn’t proven) is not a failure. Testing strategies before rolling them out instead of just relying on intuition allows us to avoid the ineffective (or even harmful) ones, which actually saves a lot of time and effort, and should therefore be treated as a win.”
Advice for making a career in Data Science
There are many opportunities to learn and practice data science on your own, such as the large number of free tutorials online, or participating in Kaggle competitions. Many cities also have data science meetups where you can hear from existing data scientists and meet other enthusiasts.
Women in Tech
Grace says, “This is a multi-faceted problem and there’s no one simple solution, but I feel we’re heading in the right direction – it’s heartening to see increasing support for not only women, but also people with families and other underrepresented groups. For example, Uber recently held a mentorship workshop with Lean In Singapore, and fitted the new offices with mother’s rooms.”