We believe a large part of building an effective insurance company can be solved with a principled quantitative framework. We are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.
One of our core data science challenges is understanding driving behavior from smartphone sensor data. Dealing with the flood of sensor data requires us to build large-scale systems that ride the big data wave rather than being submerged by it. There is no shortage of computational and statistical challenges to tackle. In addition to telematics risk-scoring, the Data Science team works on core problems in pricing model development, reserving methodology, marketing, and strategic business forecasts.
Root is looking for an experienced Senior Data Scientist to help drive the future of our team. This includes taking high-level business challenges, translating them into a concrete, quantitative framework, and shepherding solutions from R&D into production. A Senior Data Scientist is expected to join the team with years of industry or academic experience in directly relevant topics.
Responsibilities
- Strong individual contributor with interest in growing a team of data scientists.
- 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.
Minimum Qualifications
- Masters or PhD in a quantitative discipline with 5+ years of applying advanced quantitative techniques.
- 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.
- Strong programming skills in R or Python
- Knowledge of data science technical stacks