Data Scientist II, Interventions

Root Insurance

8d•$116,664 - $145,830•Remote

About The Position

We believe that a disruptive insurance company must have a principled quantitative framework at its foundation. At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning (ML) methods to problems in the insurance industry. A Data Scientist II at Root is responsible for the end-to-end development of statistical methods and algorithms. This includes taking high-level business challenges, translating them into a concrete, quantitative framework, and shepherding solutions from R&D into production. Data Scientists typically work on cross-functional teams, regularly engaging with the members of various departments including Product, Engineering, and State Management. The Interventions data science team leverages ML techniques to target interventions that improve customer lifetime value, whether via mitigation of risk (e.g., fraudulent claims, bad debt) or by enhancing conversion and retention in our most profitable customer segments. We are looking for a Data Scientist II who will monitor and improve our suite of ML models, seek out new applications for ML to create business impact, and conduct rigorous experiments to validate new targeted interventions. Salary Range: $116,664 - $145,830 (Bonus and LTI Eligible) Root is a “work where it works best” company. This means we will support you working in whatever location that works best for you across the US.

Requirements

Advanced degree in a quantitative discipline (PhD preferred) and/or 2+ years of applying advanced quantitative techniques to problems in industry
Strong demonstrable knowledge of topics such as statistical modeling, machine learning, and numerical optimization
Exceptional communicator and storyteller with strong data visualization skills
Strong programming skills with experience using modern packages in Python
Experience with databases and SQL
Demonstrated experience building, validating, and applying statistical machine learning methods to real world problems
Ability to work independently with a strong ownership mentality, taking initiative to find, prioritize, and be accountable for the highest impact work
Ability to frame functional problem statements for the next 1-2 months, consistently making good decisions about the right path to follow in a well-defined problem space

Nice To Haves

Experience using version control (Git) and cloud computing (AWS)
Insurance industry experience

Responsibilities

Create robust predictive models for use in targeted interventions, using modeling techniques such as LightGBM
Apply principled methods to translate model segmentation gains to improvements in key financial metrics
Learn the required tools to get the job done, e.g., AWS (EC2, SageMaker, S3), Git, etc.
Build data science pipelines to quickly iterate on research ideas and put them into production
Effectively communicate insights from complex analyses
Take end-to-end ownership of problem domains and continuously improve upon quantitative solutions

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