Progressive Insurance

Position : Data Scientist Senior or Lead


Job Description

As a Data Scientist on the UBI (Usage Based Insurance) team the primary responsibility of this position is to analyze the data and process to research the customer driving behavior, and its predictiveness to claim frequency and loss ratio, improve the UBI algorithm and product design, incorporate additional data sources into our UBI program, and monitor the performance of the UBI business. As a UBI data scientist you will use python to develop driving features, build predictive models, as well as other UBI analysis. Python is a very powerful tool in this space due to the need for flexibility, parallelization, and distributed computing. UBI is at the forefront of auto insurance pricing. It allows us to use when/where/how a vehicle is driven, along with other traditional variables, to calculate the insurance rate.

Sponsorship for work authorization for this position is available for candidates who already possess an H-1B visa.


Must-have qualifications:

  • Bachelor’s degree with quantitative focus in Econometrics, Statistics, Operations Research, Computer Science or related field (e.g. Mathematics) and two years of relevant experience in Statistical/Quantitative Modeling and/or Machine Learning tools (Python, R, etc.) and in using various database tools (e.g. Hadoop, SQL) processing large volumes of structured and unstructured data
  • Instead of a degree, four years of relevant experience listed in above bullet
  • Two years of experience with mathematical model creation and/or evaluation, hypothesis testing and experimental design

Preferred skills:

  • Ability to incorporate new data sources into existing models
  • Good knowledge in cloud systems such as AWS for big data needs
  • Experience working with big data and programs in the CLI
  • Familiar with the data extraction, manipulation analysis tools such as Python, SAS, R, and Excel
  • Understanding of the basic data analysis methodologies such as multivariate regression

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