Head of Data Analytics

  • 35493
  • Non-Life - Data Science
  • |
  • California, United States
  • |
  • Jul 16, 2019
Insurance
RESPONSIBILITIES
  • Takes a proactive approach to anticipating and solving problems.
  • Ensures that employees understand their level of accountability and takes appropriate action to ensure employees fully understand roles, responsibilities and performance standards and provides ongoing feedback and support.
  • Works under minimal supervision.
  • Carries out responsibilities professionally and in accordance with the Company’s policies and applicable laws.
  • Work closely and communicate effectively with internal and external clients, underwriting, project/program managers, actuaries and other departmental teams to turn data into critical information and knowledge that can be used to make sound organizational decisions.
  • Ability to present findings back to the business by exposing assumptions and validations in a way that can be easily understood by business counterparts.
  • Use predictive analytics and modeling to increase and optimize financial business objectives.
  • Collaborate with different functional teams to identify business requirements, develop and implement models and monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance.
  • Conducts exploratory data analyses and experimental designs to understand market trends and recommend suitable strategies to meet company goals and targets.
  • Research available and appropriate data and prepare data sets for modeling applications.
  • Effective communicate data results with senior and executive leadership.
QUALIFICATIONS
  • Minimum seven years of FCAS or ACAS experience building predictive statistical models.
  • Degree in Statistics, Mathematics, Computer Science or other related quantitative field.
  • Previous experience in Management Information Reporting desired.
  • In-depth insurance industry/business knowledge required.
  • Fellow or Associate of the Casualty Actuarial Society strongly preferred. Predictive Modeling accreditation desirable.
  • Experience with statistical computer languages and fluency with big data tools to manipulate data and draw insights from large data sets is required.
  • Experience working with and creating data architectures.
  • Highly sophisticated ability to create and use advanced statistical and predictive modeling techniques and concepts.
  • Proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing and optimization algorithms.
  • Ability to propose solutions to loosely defined business problems by leveraging pattern detection over potentially large datasets.
  • Ability to apply common sense understanding to carry out instructions furnished in written, oral, or diagram form.
  • Knowledge of insurance industry, programming, and current technologies used in predictive modeling and statistical analysis.
  • Logical thought process and problem-solving ability; able to effectively gather and analyze information skillfully and provide solutions in complex situations.
  • Seeks root causes.
  • Anticipates long-term problem areas and associated risk levels with objective rationale.
  • Identifies items requiring additional information or action and addresses and communicates appropriately.
  • Solid team-builder, be result oriented, a creative and strategic thinker with innovative problem-solving skills.
  • Approachable and trustworthy with innate ability to form strong and meaningful bonds with executives and stakeholders.
  • Leads discussions with senior leaders and internal partners to support strategic planning and decision making.
  • Solicits authoritative perspectives and advice prior to approving recommendations.
  • a drive to learn and master new technologies and techniques.
  • Employs sound judgment in determining how innovations will be deployed to produce higher return on investments.
  • Analyzes previously used concepts, processes or trends to devise new efficiencies or approaches not obvious to others.
  • Thinks expansively by combining ideas in unique ways or making connections between disparate ideas.
  • Studies industrial, business and technological trends and forecasts.
  • Differentiates data sources for validity, reliability and credibility.
  • Assesses the validity of business strategy recommendations against trend and historical data.
  • Debates opinions, test understanding and clarifies judgments.
  • Ability to explain and illustrate the context of multiple, complex inter-related situations to a wide and diversified audience.