Actuarial - Modeling Data Engineer

  • 33958
  • Non-Life - Data Science
  • |
  • Ohio, United States
  • |
  • Jul 24, 2018
  • Support predictive analytics efforts in small business, loss control, premium audit, claims, commercial lines and personal lines.
  • Produce actuarial/modeling analyses related to premium adequacy, rate adequacy, competitive market position, model bias, residual lift, etc.
  • Design and implement data cleansing processes.
  • Understand the data needs of predictive modelers and work with them to determine appropriate data sources; and to construct appropriate queries, code and/or databases to obtain, scrub, aggregate and deliver the relevant data.
  • Establish and maintain periodically updated data sets from a variety of internal and external sources.
  • Gain high-level knowledge of actuarial techniques for rate-making and insurance modeling.
  • Explore new data systems as they become available and document the contents and limitations of each.
  • Develop, maintain, improve, and document code, macros, and databases used within the Predictive Analytics unit.
  • Contribute to discussions with IT regarding architectural design of data systems, representing the needs of Predictive Analytics.
  • Gain expertise in all data systems available to Predictive Analytics and understand the contents and limitations of each.
  • Establish and maintain libraries of optimized code for common tasks.
  • Re-engineer low-efficiency processes through the practice of continuous improvement.
  • Develop prototype reports to meet the needs of various departments for modeling output, and work with Business Intelligence to transition reports to production.
  • Assist with the evaluation of external data sources.
  • Serve as an advisory resource for modelers to learn about data sources.
  • Plan and facilitate working sessions with cross-functional teams to accomplish goals.
  • Help to support modelers’ use of modern modeling tools such as R and Python, and modern data storage and acquisition systems such as Hadoop and Spark.
  • bachelor’s degree in a quantitative field.
  • Three-five years of highly technical experience at a property/casualty insurance company.
  • Desire to learn about the business of insurance, actuarial science and predictive analytics.
  • Proficiency with Microsoft Excel.
  • Experience with: VBA, SQL and writing SAS code; other programming languages, such as Java or C#; R and/or Python; Hadoop file system; CFC data sources, such as Reporting Systems, eCLAS, Diamond, AMPS, CMS, BW, PAD.
  • Ability to: quickly understand and assess data structures; work both independently and as part of a team; solve problems systematically and creatively; research data contents, evaluate data quality, scrub data and validate results; write business requirements and to relate business and actuarial needs to data construction and coding requirements; articulate complex technical concepts to non-technical clients.
  • Have a thorough understanding of insurance concepts and P/C insurance products.
  • Have excellent documentation standards and communication skills.