Silvia is a Data Scientist in the Customer Success team at Stemly. She studied Computer Science and Physics at Yale-NUS College in Singapore. When she is not busy improving the accuracy of a demand forecasting model, she is probably advising customers on how Machine Learning can improve their processes.
Can you tell us a little bit about your background? Where did you grow up?
I grew up on the island of Sardinia, Italy. When I was 17, I won a scholarship that funded my International Baccalaureate at the United World College of the Adriatic, in Duino (IT).
Being in an international, English-speaking community encouraged me to continue my education outside of Italy. I had heard that Yale and NUS had just funded a new college in Singapore; the startup spirit of a new school and the new cultural environment sounded like the perfect challenge for 19-year-old Silvia. Quite a few years later, I am still enjoying living in the Little Red Dot, but I certainly miss the emerald water of the Sardinian beaches.
When did you realize you wanted to do Data Science and how did you pick up your expertise?
I am a compulsive problem solver. You happen to tell me you have an issue you can’t quite get right? I’d have to stop myself from starting to troubleshoot with you the moment you mention it. Initially, I wanted to become a Theoretical Physicist because I thought they solved the most difficult problems in the universe. Little did I know that as long as there is no human behavior involved, things can be quite predictable – at least in the form of probabilities. Moreover, I needed to see the impact of my solutions ASAP. I am not very patient, it turns out.
Data science has it all: the challenge of unsolved problems, the mystery of the myriad of patterns hidden in the data, and the continuous learning brought by the stakeholders ready to share their expertise with me and with my algorithms.
My courses in statistics, algorithms, and machine learning helped me learn the fundamentals. Working for SAP’s R&D team as a Machine Learning Research Intern consolidated that knowledge and confirmed that, yes, Data Science was the right choice for me. We built a recommender system for learning resources that is still being used in companies worldwide! After the internship and during my final undergraduate year, I pursued a research project in computer vision. For a year, I worked on developing an algorithm that can bring dark pictures back to ‘light.’ It was a challenging and rewarding experience that taught me a lot about resilience and time management. Having worked on ML for images, language, time series, and classification tasks makes me what you’d call a data science generalist. Each of these fields has its own quirks, and the more I learn, the more there is to discover. Every day is a TIL day!
What are your favorite parts about working at Stemly?
I like that my work can bring so much value to our customers in terms of time saved and increased accuracy. Seeing that our team’s effort can outperform the best demand planners out there in a fraction of the time feels amazing!
How have you been able to learn and grow at Stemly?
Being exposed to various customer use cases in different verticals means that there is never a dull moment. To translate the business problems into technical solutions, I need to understand more about the company, its supply chain, and finance practices. As I see more and more use cases, I grow more knowledgeable about which solutions fit best for different classes of problems. I can then be more effective in suggesting the right approach to the Customer.
What do you like to do in your spare time?
Before COVID, the answer would have definitely been ‘singing.’ I was a Soprano in several choirs around Singapore and funded the Yale-NUS Chamber Choir. I love singing in a group, the feeling when your voice harmonizes with someone else’s it’s just indescribable. With COVID, however, there has not been much group singing. Another passion of mine is definitely eating and then swimming to burn off those calories. I am the go-to person in my circle of friends for any food recommendation. Even my Singaporean friends ask me where to find the best bowl of Laksa!
What technological problem do you think it worth solving and why, if you had unlimited resources?
This is my partner’s favorite question: he always says ‘artificial photosynthesis.’ For me, I’d have to go with something that I could actually contribute towards the ‘diagnose-and-cure-all-diseases’ machine you see in Sci-fi movies. I believe AI has a lot of potential there.
Everyone’s either a sweet or savory person, which one are you? And what’s your favourite cake/dish?
I do not discriminate – it all depends on how I am feeling that day. One of my favorite desserts is apple pie; I love the apple and cinnamon combination!
Apple pieArtificial IntelligenceComputer ScienceCustomer SuccessData ScientistDuinoInternational BaccalaureateItalyMachine Learning ResearchPhysicsSardiniaSingaporeSopranoTheoretical PhysicistUnited World College of the AdriaticYale-NUS College