We believe a large part of building an effective insurance company can be solved with a principled quantitative framework. Our Data Scientists are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.

One of our core data science challenges is understanding driving behavior from smartphone sensor data. Dealing with the flood of sensor data requires us to build large-scale systems that ride the big data wave rather than being submerged by it. There is no shortage of computational and statistical challenges to tackle.

We're looking for a Data Scientist to help turn our driving data into new guidance for our pricing and underwriting. As part of our "Telematics Scores" team, you'll uncover new telematics features that may indicate future risk. By identifying and owning research projects, you'll build new models and insights to more accurately score our drivers. Furthermore, you'll have an opportunity to innovate using telematics data outside of pricing and underwriting use cases.

Responsibilities:

• Improve our telematics pricing and underwriting algorithms through principled data science methods.
• Develop new ways to extract risk information from granular telematics signals.
• Pinpoint abnormal user behaviors.
• Deploy and measure the performance of your models in a production environment.

Minimum Qualifications:

• PhD in a quantitative discipline and/or 3+ years of applying advanced quantitative techniques in industry.
• Strong demonstrable knowledge of topics such as statistical inference, numerical linear algebra, machine learning, and numerical optimization.
• Strong programming skills with experience using modern packages in Python. SQL a plus
• Exceptional communicator and storyteller.
• Demonstrated experience building, validating, and applying statistical machine learning and signal processing methods to real world problems.