The position.

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

We're looking for a Data Scientist to help build and evaluate our pricing models to enhance our risk segmentation. Working alongside a combination of actuaries and data scientists, you'll leverage modern statistical techniques to build on our pricing capabilities and more accurately segment our customers. You'll also establish KPIs to explain model performance, communicating findings and trends to key stakeholders across the business.

Root is a “work where it works best” company. Meaning we will support you in working in whatever location that works best for you across the US. We will continue to have our headquarters in Columbus and offices in other locations to give more flexibility and more choice about how we live and work.

What you'll achieve.

  • Build and evaluate pricing models and risk segmentation plans
  • Enhance evaluation pipelines to translate model results into KPIs
  • Leverage internal and external data sources to engineer novel features to enhance risk segmentation
  • Build data processing pipelines to quickly iterate on research ideas and put them into production
  • Take end-to-end ownership of problem domains and continuously improve upon quantitative solutions

What we're looking for.

  • Advanced degree in a quantitative discipline and/or 3+ years of applying advanced quantitative techniques to problems in industry, actuarial pricing experience preferred
  • Strong demonstrable knowledge of topics such as inferential statistics, machine learning, and numerical optimization
  • Exceptional communicator and storyteller
  • Strong programming skills with experience using modern packages in R and Python
  • Demonstrated experience building, validating, and applying statistical machine learning methods to real-world problems

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