Head of Analytics

  • 35978
  • Non-Life - Data Science
  • |
  • Ohio, United States
  • |
  • Oct 4, 2019
Insurance
RESPONSIBILITIES
  • Organize and lead analytics center of excellence.
  • Participate in a data council to provide insight and input to the data strategy.
  • Set annual project plan based on input from Segment heads and Operating Units. Projects focus on enhancing profitability and business functions but also may be exploratory to answer business questions.
  • Develop individual project plans, timetables and deliverables. With IT and Business ensure that plan addresses data collection, model iteration, validation and operationalizing both model and model results. Regularly communicate project status vs plan.
  • Keep a log/catalogue of projects, code and models so that knowledge can be shared.
  • Work with Business and IT to ensure that Big Data platform is appropriately structured, accessed, protected and utilized. Follow procedures established.
  • Establish best practices for model use and validation as well as for basic analytics for business intelligence purposes.
  • Work with Business and IT to: expand availability and use of internal data; develop strategy for data access for analysis and leverage data across; and facilitate the evolution of self-service data preparation and analytics. Contribute to decisions made about information value, quality and reliability.
  • Work with Business and IT to expand availability and use of external data. Participate in research on data available from external sources and support efforts to acquire and make usable.
  • Keep abreast of latest machine and deep learning tools, methodologies and data sets. Test and evaluate.
  • Provide input and counsel to operating units. Provide quality control via peer review Operating Unit models and algorithms, etc.
  • Provide regular forums for sharing of information. Provide training as needed for actuaries and citizen data scientists on R and other tools.
  • Build team of data scientists, wranglers, munchers, etc. either at corporate or in shared service areas. Define job roles, recruit candidates and then manage directly or indirectly team of data science professionals.
QUALIFICATIONS
  • Master's degree or Ph.D. in data science, computer science, business analytics, applied mathematics, statistics, actuarial science, engineering or related fields.
  • 15+ years in data science and analytic leadership position, ideally with product management or actuarial reserving and pricing background as well.
  • Track record of working with big data and social data.
  • Data Science, Artificial Intelligence, Machine Learning Deep Learning.
  • Actuarial Science.
  • Experience in integrating complex, cross-corporate processes and information strategies, and/or designing strategic metrics and scorecards.
  • Proven data literacy - ability to describe business use case/outcomes, data sources and management concepts.
  • Complex project management and change management.
  • Excellent interpersonal skills to work across business lines and functions.
  • Excellent oral and written communication skills, including ability to explain complex technical subjects to business leaders, as well as business concepts to technologists, be able to sell ideas and processes to all levels.
  • Proven record of leadership including ability to balance team and individual responsibilities: building teams and consensus, getting things done through others not directly under his/her supervision.
  • Gravitas to work with senior management.
  • Ability to effectively drive people process and technology change in dynamic complex operating environment.
  • Insurance experience, Commercial and Personal P&C Insurance and Reinsurance.
  • Technology/Tools/Algorithms: R, Python, H20, SAS, Random Forest, GLM, Gradient Boosting, Regularized regression, Word2Vec, Hadoop, Hive, SQL, ETL processes, Docker, Kubernetes.