Data Science Intern
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.
Root is seeking an intern for our data science team who’s eager to get their feet wet in the industry. In this role, you’ll be dealing with massive data sets, extracting raw time-series features or building high-dimensional models. You’ll be procuring and cleaning a wide variety of data sets and finding ways to make sense of inconsistent results, errors, and missing information. Potential teams you’ll be working alongside include:
Pricing R&D
- improving segmentation with more powerful predictive models
- exploring novel approaches to fraud analytics
- informing strategic business decisions with analyses of unit economics
LTV Analytics
- improving the predictive power of our retention models
- answering counterfactual questions with LTV models
- exploring and constructing new features for inclusion in downstream predictive models
Responsibilities:
- Applying principled methods to quantitative insurance challenges in areas such as telematics risk scoring, pricing, reserving, and estimating customer lifetime value.
- Learning the required tools to get the job done, e.g. Python, R, Spark, SQL, etc. Building data processing pipelines to quickly iterate on research ideas and put them into production.
- Effectively communicating insights from complex analyses.
- Taking end-to-end ownership of problem domains and continuously improving upon quantitative solutions.
Qualifications:
- Advanced degree in a relative quantitative discipline (statistics, mathematics, physics, etc.)
- A strong mathematical background, particularly in statistics and linear algebra
- Advanced demonstrable experience in building, validating, and leveraging machine learning models
- Strong programming skills, preferably in Python and/or R
- Experience with academic research OR past internship experience in a relevant field