Jason joined World Wide Technology in 2014, coming with two decades of experience in analytics and software development. Jason has led modeling projects in financial services, healthcare, pharmaceutical, insurance, retail and marketing science sectors, with special emphasis on the development of customized solutions. He has also worked with many Fortune 500 companies.
Jason was previously a director at Opera Solutions, where his responsibilities focused on the development of solutions that called for novel algorithms. Prior to that, Jason served as a senior manager at FICO, heading a team on innovating payment card fraud detection products. Jason has also worked at J.D. Power and Associates as a senior scientific modeler, developing marketing mix and incentive planning solutions for automotive manufacturers.
As Chief Scientist of the Big Data practice at WWT, Jason is responsible for the development and delivery of big data analytics solutions.
Jason has a BS in Electrical Engineering, an MS in Mathematical Physics from Universidad de Chile and a Ph.D. in Theoretical Physics from Stanford University.
Q&A with Jason Lu
- Tell us about your background and how you got into technology.
- I’ve had a colorful background that stretches a few continents and cultures. But a common theme early on was my interest in science and technology. I built radio receivers and transmitters around age 10, and I loved to conduct physics experiments during my high school years. On the serious side, I have published research work in theoretical particle physics on quarks and Higgs boson. On the lighter side, I have produced some best-selling educational computer games for children. Somewhere in between, I have developed analytic models for business clients. Today at WWT, I help industrial clients with their big data analytics needs through building IoT (Internet of Things) and sensor-analytics solutions. I am grateful that I have not had one single boring day in my life, and I am excited to live in this important moment in history.
- Describe your role at WWT.
- My responsibility is the development of innovative solutions using big data analytics to help WWT’s clients tackle business and manufacturing problems. Among other areas, I am particularly interested in working with IoT (Internet of Things) and sensor analytics.
- What innovation is happening in big data that has you really excited?
- The convergence of a wide spectrum of companies, on the utility of big data analytics. In one week, I may be looking into helping large-scale retailers on profiling their customers, while in another, I may be looking into solutions on unmanned vehicles, working with customers in the mining industry, figuring out how to increase oil/gas production, developing GPS geospatial information solutions, learning about weather forecasting, learning how contact lenses are made, evaluating hospital operations, diving into the latest development in human genomics, helping the development of Smart City initiatives or looking into anomaly detection in network security. The list is endless and I love that!
Some people tell me technology singularity is coming, but in my opinion, “the singularity” is already here. It’s a new and exciting world, and it requires a new perspective to understand the massive amount of information that we are being bombarded with, each and every day. It is a privilege working with many talented people in this field. It’s not the machines that make me excited, but the people with their creativity.
- Describe a recent interaction with a customer that lead to solving a problem.
- We have been working with a mining industry client to detect early warning signs in their haul truck’s engine operation, and helped them to prevent unexpected engine failures. We have also linked their geospatial information to other sensor data, and developed advanced analytics to help them optimize and visualize haul cycle operations. With an oil and gas client, we have developed well-log analytic solutions to help them find untapped reservoirs. Translating our clients’ unique expertise and knowledge, and incorporating them into advanced machine-learning algorithms, is what makes what once was considered a dream become a reality.