The Data Standard
In this episode of The Data Standard, host Catherine Tao talks about mindfulness with data with Shae Selix, Senior Data Scientist at Calm.
Calm is on the mindfulness mission, and it is quite interesting to discover how it interacts with data science. Data science can answer some of the trickiest questions, for instance, which one of the users is the best in terms of prospecting. That’s why Calm leverages data science when making marketing decisions.
Calm is also a subscription-based app. Subscription calls for analytic testing and predictive modeling, both of which are data science areas.
However, Calm’s primary mission is to enable mindfulness with data through meditation and sleep content personalization. When two completely different teams – the mindfulness and data science team – come together, everything is possible with data.
Fine-tuning the content recommendation system with A/B testing and feedback proves invaluable for driving engagement and delivering a better experience to users.
Tune in with Catherine Tao and Shae Selix to find out more about mindfulness with data, how a managed deep learning service enabled Calm to offer personalized content, and what challenges the data science team had to overcome during the development process.
Meet The Host
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
Senior Data Scientist at Calm
Shae Selix is a data enthusiast. He started out working as a GIS analyst managing data. He continued his work with data at Salesforce, where he was on a team working on the Einstein machine learning platform. He is passionate about data science, yoga, and meditation, so it’s no surprise to see him working as a data scientist at Calm for over a year.