What are the responsibilities and job description for the Assistant Director, Data Science position at Liberty Mutual Insurance Group?
Build out team's geospatial big data infrastructure using geospatial data science techniques & software such as Snowflake, dbt, and Python. Build geographic risk segmentation models using geographic data science modeling techniques. Apply insurance knowledge to conduct multiple forms of statistical analyses, such as building loss cost models and creating geographic loss data. Apply spatial data techniques to create usable outputs from geographic risk segmentation models at multiple geographic granularities. Enable the business to make clear tradeoffs between and among choices, with a reasonable view of the likely outcomes. Responsible for larger components of projects of moderate to high complexity. Guide aspects of project design as a technical consultant for the team. Employer will accept a Ph.D. degree in Statistics, Mathematics, Economics, Actuarial Science, or other scientific field and two (2) years of experience in the job offered or in an Assistant Director, Data Science -related occupation. Alternatively, employer will accept a Master's degree in Statistics, Mathematics, Economics, Actuarial Science, or other scientific field and four (4) years of experience in the job offered or in an Assistant Director, Data Science -related occupation or Bachelor's degree in Statistics, Mathematics, Economics, Actuarial Science, or other scientific field and five (5) years of experience in the job offered or in an Assistant Director, Data Science -related occupation Geospatial data science methodologies and applications including geospatial big data management and spatial statistics. Property & Casualty insurance data science techniques including working with exposure, loss, and premium data. Statistical modeling methodologies and applications including Generalized Linear Models (GLMs) and Gradient Boosting Machines (GBMs). Insurance predictive modeling experience in third-party software including Emblem and Earnix. Python data science libraries including pandas, geopandas, and numpy. SQL based data management, including Snowflake and dbt. Data analytics and interpretation through exploratory data analysis and spatial data analysis techniques. Utilizing AWS cloud services including EC2 and S3. Demonstrated understanding of MLOps standards including unit testing and virtual environments. Experience may be gained during graduate program.