Data and Analytics Officer

  • 36676
  • Non-Life - Data Science
  • |
  • Connecticut, United States
  • |
  • Jan 22, 2020
  • Create and lead Data, Information and Analytics.
  • Organize and chair advisory board to execute the company data, information and analytics strategy, provide sponsorship and oversight for governance policy and analytic project success.
  • Define, coordinate and maintain company Data Warehouse the single source of truth structured data required for financial reporting and regulatory compliance.
  • Create policies and controls for appropriate protection of information assets through life cycle, from acquisition or creation to destruction. Define, manage and advance information management principles. Develop policies and programs for stewardship and custodianship of data, controls for master data and metadata management.
  • Oversee integration and staging of data, and development and maintenance of data lakes, data warehouse and data marts used throughout the company. Design the use strategy, governance and process for the Hadoop Data Lake.
  • Expand availability and use of internal data. Make decisions about information value, quality and reliability. Develop strategy for data access for analysis and to leverage data across the company. To facilitate the evolution of self-service data preparation and analytics.
  • Expand availability and use of external data. Research and keep current on data available from external sources and lead efforts to acquire, make usable and provide access. Ensure quality, traceability, timeliness, usability and cost-effectiveness.
  • Set annual project plan based on input from Segment heads and Operating Units. Projects focus on enhancing profitability and business 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.
  • Establish best practices for model use and validation as well as for basic analytics for business intelligence purposes.
  • 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.
  • Responsibilities are carried out in conjunction, with IT, Compliance, Finance, Actuarial and Other Business Leaders.
  • Degree in business administration, computer science, information science, data science, business analytics, applied mathematics, statistics, actuarial science, engineering or related fields.
  • 15+ years in data science, business information and analytics leadership positions.
  • Background in business, financial, information or IT management. Ideally with Property & Casualty insurance, reinsurance.
  • Ability to effectively drive people process and technology change in dynamic complex operating environment
  • Complex project management and change management skills.
  • Experience in integrating complex, cross-corporate processes and information strategies, and/or designing strategic metrics and scorecards.
  • 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.
  • Track record of working with Big data and Social data; Artificial Intelligence, Machine Learning and Deep Learning.
  • Proven data literacy - ability to describe business use case/outcomes, data sources and management concepts.
  • Gravitas to work with senior 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.
  • Technology/Tools/Algorithms: R, Python, H20, SAS, Random Forest, GLM, Gradient Boosting, Regularized regression, Word2Vec, Hadoop, Hive, SQL, ETL processes, Docker, Kubernetes.