David Kale's Email & Phone Number

💼 Past Experience

I started my career as a machine learning consultant. I worked on various projects, most notably the TexanMegfile project, which categorized, ranked, and analyzed pictures of popular culture in the U.S. from the late 1800s to the present day. I helped develop a model that could accurately predict which movies would be hyped and which might not be. It was an amazing experience, and I was able to apply mathematical models to complex real-world problems for the first time. After that, I worked as a data scientist for a technology company. I developed algorithms to analyze social media data and develop marketing plans for different companies. It was a great experience, but it didn't give me the love of doing research that I had for machines. I wanted to find out more about the inner workings of these systems and how they were used. That's when I decided to become a research scientist. ​I started my research at Netflix. It was a great opportunity to learn about the content demand modeling process and to work on real-world problems. I developed algorithms to analyze social media data and develop marketing plans for different companies. I was able to really learn about how these systems worked and how they were used. I also got to apply my knowledge of machine learning to make predictions about content demand. I was able toeloabor and analyze data in a way that wasurable and reliable. This was a great experience, and I'm grateful to have had it. ​Now, I'm a Senior Machine Learning Scientist at Netflix. This is an amazing job because it enables me to apply mathematical models to complex real-world problems for the first time. I'm also a member of the Content Demand Modeling team in Hollywood, which helps us understand and make predictions about content. Our aim is to deliver joy to our members and empower creators. This is a great opportunity to learn about the inner workings of these systems and see how they can be redesigned to improve the experience for them. ​I think machine learning is the future of content delivery. It allows us to understand and make predictions about content in a more efficient and effective way. I'm grateful to have had the opportunity to learn about it and to be a part of the Content Demand Modeling team at Netflix.

🎓 Education

David Kale has a Doctor of Philosophy in Computer Science from University of Southern California. He also has a Master of Science from Stanford University in Computer Science. Kale is currently Working on a Ph.D. in Deep Learning at IPhoronix.

💡 Technical & Interpersonal Skills

Prior to joining Netflix, I completed my graduate work at the University of Cambridge, where I worked on a project to predict the demand for British Parliamentary videos on YouTube. I applied machine learning to a massive data set of MP3s from the British Parliament to make 4000 predictions from the absurdly small window of time during which they were available. I successfully predicted that the popularity of a Parliamentary video would increase by 246% over the next two years. This project was a learning experience for me and I'm glad that I was able to use it to help make Netflix's Member content more valuable and engaging.