Headquarters: Northbrook, IL, USA
Data Analytics team is responsible for building Advanced Analytics and Business Intelligence solutions/capabilities and contributing to Roadside Services Business objectives by identifying and developing growth and profitability opportunities that will enable Allstate Roadside Services to generate profitable market share growth.
Job Summary The role is responsible for leading the use of data to build predictive models and make decisions. This role must interpret and understand the Allstate Roadside Services business problems, analyze data requirements, recommend & build appropriate predictive models , coordinate and execute the projects, and communicate and share the business insights and actions recommend.
•Build best-in-class predictive models and decision tools to address Allstate Roadside Services business needs by leveraging state-of-the-art machine learning and statistical algorithms •Identify new areas of data, research and build predictive models to improve Network Operations •Work on data and problems across ARS departments to drive improved business results through designing, building, and partnering to implement models •Manage data and data requests to improve the accuracy of our data and decisions made from data analysis •Use and learn a wide variety of tools and languages to achieve results (e.g., Business Objects, Tableau, R, Python, Hadoop, Oracle) •Use best practices to develop statistical, machine learning techniques to build models that address business needs •Develop frameworks/prototypes that integrate data and machine learning/predictive modeling to make business
•Master’s degree or PhD preferred in a quantitative field such as statistics, mathematics, computer science, finance, or economics. Proven experience in using statistical modeling and/or machine learning techniques to build models that have driven company decision making •At least 3 years (over 5 years preferred) of predictive modeling experience or equivalent skills & ability. •Must be technically proficient, mathematically agile, business savvy and good at communication •Proven experience in managing and manipulating large, complex datasets