Actuarial Data Analyst

  • 34852
  • Non-Life - Data Science
  • |
  • Minnesota, United States
  • |
  • Mar 14, 2019
Insurance
RESPONSIBILITIES
  • Collaborate with internal customers to identify business needs; prioritize requests; develop, test and implement data analytics and predictive modeling.
  • Expand understanding and use of data within the organization by presenting analysis, results and recommendations to reduce costs and increase revenue.
  • Research and test new modeling and implementation techniques to optimize flow of analytics information to internal consumers.
  • Assist in development and implementation of tools and software additions to support BI goals.
  • Recommend and implement algorithm deployment and management strategy for predictive models.
  • Identify, develop, and implement analytics strategies where precedents and procedures may not exist.
  • Work cross functionally to identify internal customer data analytics needs.
QUALIFICATIONS
  • 3+ years of Data Modeling or similar experience.
  • Experience in Property & Casualty Insurance is preferred.
  • Demonstrated experience working with large relational data sets.
  • Working knowledge in at least 1 statistical language developing and implementing predictive models.
  • Proven ability to communicate complex technical information in common language to foster teaching and analytics guidance to internal customers.
  • Advanced experience in analytics, data cleaning, and predictive modeling.
  • Proven ability to work in a self-directed or team environment with little guidance when necessary.
  • Demonstrated ability to adapt to and lead rapid change.
  • Bachelor’s degree required.
  • Proficient use of various core systems, office and computer equipment and software packages.
  • Ability to write queries in SQL.
  • Experience with dashboard development is preferred.
  • Experience implementing analytics and predictive models into dashboard or stand-alone solutions is preferred.
  • Familiarity with implementing analytics/predictive models into web platforms is preferred.
  • Familiarity with non-relational language is preferred.