In our next Lucidite Spotlight, we interview Shuning Wu, Lucidum’s Data Science Lead. Shuning has taken a keen interest in analytics and data science since 2007. When he’s not creating innovative products and solving problems within them, you can find him in the kitchen whipping up authentic Cantonese dishes for his family.
Tell us a bit about yourself. What was your background prior to Lucidum and what’s your current role and day-to-day at Lucidum?
I got my PhD degree in Industrial Engineering in 2007. After that, I started my career at Insurance Service Office (ISO) in San Francisco, building different insurance, workers compensation, and healthcare predictive models. Then I joined Symantec, to help develop data-driven security intelligence to detect and prioritize malicious assets and users, which is where I began my journey in the security industry. In 2017, I took a slight shift and worked as a principal machine learning scientist at Nordstrom, building advanced ML-Sort machine learning models to provide better product personalization and recommendations for online customers. Later, I joined Splunk and further sharpened my skills on security innovation by designing and deploying sophisticated anomaly detection machine learning applications for the Splunk security teams.
As a founding data scientist and engineer at Lucidum, I design, build, and deploy the entire Lucidum data pipeline and patent-pending machine learning engines. I am also responsible for multiple roles across product development, roadmap planning, project management, team coordination, and customer support.
How have you brought what you’ve learned through your time at Splunk, Symantec, and Nordstrom to Lucidum?
From my previous career, I learned a lot — not only about data science and machine learning techniques — but also on security domain knowledge and problem-solving skills. These lay out a solid foundation for my current work at Lucidum.
What is the best career lesson you’ve learned so far?
Always learn more about the data. For example, which business environment does the data come from? How is the data being collected? What is the meaning of the data elements? What is the limitation of the data? Understanding the data better will not only help to build better machine learning models, but also help to understand customers’ business problems, pain points, and ultimately provide better products to customers.
What is it you enjoy most about your job at Lucidum?
Involvement in the whole product development process. In my previous roles, I used to only work on one small piece of a product, or solve one specific issue of a bigger problem. At Lucidum, I am able to create a complete product from scratch and learn so much more every day.
What’s something that excites you about your future with Lucidum?
Witnessing Lucidum to be the market leader in asset discovery and management, and providing excellent products with an amazing customer experience.
How do you spend your time outside of the office?
Playing Nintendo Switch games with my son!
What’s a fun fact about you many people may not know?
I am a pretty good chef of authentic Cantonese dishes.