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

Advanced degree in a quantitative discipline (PhD preferred)
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
Preferred but not required: Experience using version control (Git) and cloud computing (AWS)
Insurance industry experience

Data Scientist II, Interventions

Key skills