Amazon's recent offer to acquire Zoox places them squarely in the Autonomous Vehicle (AV) space, competing with the likes of Waymo, Tesla and others for movement automation of people and goods. As a reminder, Zoox is the San Francisco, California based company with a breathtakingly bold vision – to develop purpose-made autonomous electric vehicles for ride-hailing, and deliver services to consumers through this platform. Executing this vision means competing on multiple fronts with big players- automotive OEMs (designing and building cars), tech companies like Google (who has spent a decade and a fortune to develop the Autonomous Vehicle Driving System or AVS), and ride-hailing companies like Uber and Lyft LYFT . To date, Zoox has raised $1B and hired close to 1000 people, but without any revenues, they probably need 10X the investment. A recent article covers the details of the Zoox acquisition, and posits that an important driver was Zoox's expertise in computer vision.

Amazon's recent offer to acquire Zoox places them squarely in the Autonomous Vehicle (AV) space, competing with the likes of Waymo, Tesla and others for movement automation of people and goods. As a reminder, Zoox is the San Francisco, California based company with a breathtakingly bold vision – to develop purpose-made autonomous electric vehicles for ride-hailing, and deliver services to consumers through this platform. Executing this vision means competing on multiple fronts with big players- automotive OEMs (designing and building cars), tech companies like Google (who has spent a decade and a fortune to develop the Autonomous Vehicle Driving System or AVS), and ride-hailing companies like Uber and Lyft LYFT. To date, Zoox has raised $1B and hired close to 1000 people, but without any revenues, they probably need 10X the investment. A recent article covers the details of the Zoox acquisition, and posits that an important driver was Zoox's expertise in computer vision.

Knowing how to write high quality software -- the days of one team writing throwaway models and another team implementing them in production are slowly coming to an end. With programming languages like Python and R and their packages making it easy to work with data and models, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design. While "big data" is a term used way too often, it is true that the cost of data storage is on a dramatic downward trend. This means that there are more and more data sets from different domains to work with and apply models to. And yes, knowing something about at least one of the popular areas of the field that have gotten traction lately -- deep learning for computer vision and perception, recommendation engines, NLP -- would be a great thing once you have the fundamental understanding and technical proficiency.