Senior Associate Decision Scientist

Farmers Insurance Careers•Bellevue, WA

5d•$131,244•Hybrid

About The Position

Farmers New World Life Insurance (Bellevue, WA) seeks a Senior Associate Decision Scientist to provide critical decision support across the enterprise by gathering, analyzing, and synthesizing data to address business challenges. The role involves applying mathematical, statistical, and quantitative techniques to solve business problems, building descriptive or explanatory models, and leveraging data visualization tools and statistical techniques to uncover insights. The position also includes conducting exploratory data analysis, collecting and integrating data from various sources, supporting data reliability through quality checks and documentation, and collaborating with cross-functional business partners and technical teams. Additionally, the role assists in influencing solution strategies, providing data-driven insights, ensuring data integrity, resolving data quality issues, and supporting the deployment and monitoring of predictive models. Continuous learning and professional development are encouraged. A significant portion of the role involves bridging data science analytics functions with operational R&D support for customized software development, including object-oriented programming, machine learning, spatial mapping, and time-series analysis.

Requirements

Master’s degree, or foreign equivalent, in Business Analytics, Applied Data Science, or a closely related field of study.
2 years of experience in the job offered, or as a Data Analyst, Data Management Analyst, Business Analyst, Research Analyst, Data Scientist, or similar position.
2 years of experience working with applying data analysis, statistical, and mathematical modeling in the Life Insurance industry or P&C Insurance industry.
2 years of experience working with software including AWS, Sagemaker, S3, Python, R, QGIS, and Google Earth Pro.
2 years of experience working with machine learning methods including boosting, bagging, KNN, clustering, and GAM.
2 years of experience working with statistical techniques including linear models, generalized linear models, linear mixed models, time-series methods, statistical clustering, and non-parametric alternatives.

Responsibilities

Applies basic to moderately advanced mathematical, statistical, and quantitative techniques to solve medium-scale business problems that significantly impact current and future business strategies.
Builds descriptive or explanatory models or uses the output of models created by others.
Leverages interactive data visualization tools and statistical techniques such as distribution analysis, correlation analysis, outlier detection, multivariate analysis, time series, machine learning, and others to uncover hidden insights.
Conducts basic exploratory data analysis (EDA) to uncover opportunities including testing hypotheses and validating findings with business partners.
Collects data from a variety of sources and integrates and transforms disparate datasets to create cohesive datasets for analysis.
Supports data reliability by conducting quality checks, documenting data processes, and collaborating with more experienced analysts for peer review and data validation as needed.
Participates in discussions with cross-functional business partners and teams to understand and collaborate on complex business objectives.
Assists in influencing solution strategies and outcomes by leveraging a working knowledge of the business.
Uses foundational experience in data analysis to provide insights that support decision-making.
Coordinates with cross-functional technical teams including IT, data scientists, data engineers and others to ensure data integrity and optimize data pipelines.
Identifies data quality issues and collaborates to resolve them.
Assists deployment and monitoring of basic predictive models into workflows.
Stays informed about emerging decision and data science methodologies, tools, and best practices.
Applies knowledge and continuous learning to improve own performance and the quality of work produced.
Actively seeks feedback from more experienced team members and engages in professional development opportunities to enhance skills and contribute effectively to team objectives.
Bridges between data science analytics functions and operational RnD support ongoing development of customized software requiring work in object-oriented programming and specialized analytics functions involving applications of machine learning, spatial mapping, time-series, and associated scientific tasks.