We’re hiring a Data Science Intern in Remote (United States)

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:

  • Graduation expected within 12 months of start date
  • 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

Get in touch

Finding the right people to help us build Root is a top priority. Whether you would like to explore the possibility of working together or simply learn more about the position, we’d love to hear from you!

Apply now
At Root, we judge people based on the merit of their work, not who they are. Very few (if any!) people will fit every description; so if you are passionate about what this role entails, and are excited by solving real problems, we encourage you to apply; we want to learn about you, and what you can add to our team!

Who we are.
Root Insurance is the nation’s first licensed insurance carrier powered entirely by mobile. We were founded on the belief that the services you need for everyday life should serve you better. That’s why we base insurance coverages on you, not your demographic. It’s the way insurance should be. And it’s all conveniently in an app.

What draws people to Root.
We’re a venture-backed technology company. Our early success is in large part due to our unwavering standards in hiring. We recognize that our product is only as good as the people building and promoting it. We look for individuals who find solutions by going through the cycle of ideation to implementation with curiosity, rigor, and a highly analytical lens. Ask anyone who works here and you’ll hear similar reasons for why they joined:
Autonomy. For assertive self-starters, the opportunities to contribute are limitless.
Impact. By challenging the way it’s always been done, we solve problems that have a big impact on our business.
Collaboration. We encourage rich discussion and civil debate at every turn.
People. We are inspired by the collection of crazy-smart people around us

Current job openings

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If you’d like to be a part of what we are building but don’t see an opening that suits you, email us at jobs@inc.joinroot.com.