AWS, Data Analytics, Data cleaning / Data cleansing, Data Visualization, Internet of Things- IoT, Python Programming, SQL
Cambridge Mobile Telematics (CMT) is on a mission to make the world’s roads and drivers safer. Our first product launched in 2012 and pioneered mobile usage-based insurance. Since then, CMT has become the world’s leading telematics and analytics provider for insurers, rideshares, personal safety providers, and automakers, providing telematics services for 80 enterprise programs in 20 countries. Powered by mobile sensing, artificial intelligence, and behavioral science, we measure driving quality, incentivize safer driving, assist users in crashes in real time, and improve the safety for millions of drivers every day around the world.
Our data science team helps to manage and analyze telematics data collected by volunteers (typically CMT employees) who run data collection apps on their phones and installed in their vehicles. We are looking for somebody to help us better understand the power draw of in-vehicle data collections sensors. At CMT, your job as a data scientist intern will be to first collect data via small scale experiments regarding how much power our collection devices consume. You will then use that data to develop models which will determine the remaining life of in-field devices. This is a unique project which melds engineering-type measurements and data collection with data science modeling techniques.
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Commitment to Diversity and Inclusion:
At CMT, we are intensifying our commitment to provide opportunities and career growth to the underrepresented. We are focused on creating an inclusive work environment that encourages a diversity of background and thought to produce the best products and services within our industry.
Cambridge Mobile Telematics (CMT) is the world’s largest telematics service provider. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell®, gathers sensor data from millions of IoT devices — including smartphones, proprietary Tags, connected vehicles, dashcams, and third-party devices — and fuses them with contextual data to create a unified view of vehicle and driver behavior.