Sr Analyst

  • 35466
  • Non-Life - Data Science
  • |
  • Tennessee, United States
  • |
  • Jul 10, 2019
Insurance
RESPONSIBILITIES
  • Design advanced analytical conceptual solutions to key business challenges/opportunities; work within a governance construct to prioritize these opportunities; and continually seeking to improve / innovating to increase impact to the organization.
  • Collaborating across multiple stakeholders to understand business needs, translate high level business goals into advanced data/technical approaches to extract and analyze complex data to produce insights and recommendations.
  • Design and develop project requirements including working with cross-functional teams, such as establishing goals/key metrics, articulating and mapping solution requirements, determining build vs. buy/license, designing sourcing and overseeing third-party expertise and/or models as needed.
  • Develop, monitor and maintain complex advanced analytics models; test and implement models and data infrastructure.
  • Monitor and optimize the implementation and testing of predictive and/or prescriptive models.
  • Develop advanced analyses to measure the performance, effect, and value of third-party data and models.
  • Perform other ad hoc analyses to provide predictive and prescriptive solutions; assisting with special projects as needed.
  • Appropriately account for the timeliness and quality of all assignments.
  • Clearly communicate recommendations from all the above responsibilities to drive better business decisions/outcomes.
QUALIFICATIONS
  • Experience in predictive and prescriptive analytics including real-world experience in statistical analysis and modeling techniques, including model development, validation, testing and deployment.
  • Competencies typically acquired through an advanced degree in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study or may be acquired through a Bachelor’s degree and 5+ years of highly relevant experience.
  • Demonstrated proficiency in SAS, R, Python or other statistical analysis tools, required.
  • Experience processing data using SQL or other data management tools.
  • Adept at framing business questions and practices in analytic terms, and translating business requirements into corresponding datasets, analyses, models, reports and presentations to both technical and non-technical audiences.
  • Proven ability to lead and work within cross-functional teams.