Industry: Finance & Insurance•
5 - 7 years
Posted 184 days ago
Travelers’ Personal Insurance organization is seeking a Director, Analytics & Research to lead development of Predictive and Prescriptive Models to optimize customer value and reduce operational expense. This is a high-impact role where you will be leading research and development of customer models leveraging latest advances in Predictive Modeling, Machine Learning and Deep Learning. You will have the opportunity to work with many difference sources of customer data, including customer interaction data across different media channels, Voice of Customer data, text and voice data. You will also help grow and expand Customer Modeling capabilities across Travelers!
Primary Job Duties & Responsibilities
Lead and manage development of analytic models to maximize customer value and improve operational efficiency
Independently lead multiple large scale modeling projects from end to end (ideation, design, model development, model validation, model implementation, etc.)
Support model monitoring and continued improvements of the test and learn framework to ensure continued improvement of business results
Manage a team of modelers to deliver projects based on business and technical requirements and agreed upon timeline; Recruit, develop and mentor modeling team.
Be a thought leader in the domain. Continually propose innovative solutions to address business challenges.
Support best practices in customer modeling and decision simulation/optimization
Effectively communicate analytic design, insight and recommendations to senior management and business partners
Foster strong collaboration with business partners across the organization
PhD STEM (Science, Technology, Engineering, Mathematics) degree with 2 years experience or Masters STEM degree with 5 years experience or Bachelor’s STEM degree with 7 yrs or Bachelor’s degree with FCAS designation and 4 years relevant progressive statistical analysis work experiencerequired. Advanced working knowledge of modeling/research/analytics or actuarialrequired. Relevant statistical analysis work experiencerequired.
Education, Work Experience & Knowledge
Relevant work experience in directing and performing research and/or advanced analytic work (e.g. predictive modeling) in the insurance industry preferred.
Job Specific & Technical Skills & Competencies
Computer Proficiency: Ability to read/revise/review a statistical software program (e.g. R, SAS, SPSS. Ability to create advanced programs from scratch. Leading the Business: Problem Solving & Decision Making. Change Management. Influencing, Leadership, Power. Risk Taking, Innovation. Results Orientation. Business Perspective. Understanding & Navigating the Organization (Includes Collaboration). Leading Others: Forging Synergy. Communicating Effectively. Leveraging Differences. Building & Mending Relationships. Participative Management. Leading Employees. Employee Development. Leading Self: Openness to Influence. Flexibility. Self Awareness. Seeks Opportunities to Learn. Leadership Stature. Credibility. Business Acumen: Understanding and knowledge of key business knowledge areas (e.g. product, enterprise, industry, claim process and competitors). Ability to leverage business knowledge to set strategies and approaches to execution. Critical Thinking: Ability to take action in solving problems while exhibiting judgment and a realistic understanding of issues; ability to use reason; review facts, identify inconsistencies and weigh options; ability to make logical and timely decisions that address the right issues. Modeling: Ability to develop advanced models and interpret model results. Statistics: Understanding of advanced statistics underlying data models. Ability to apply new statistical procedures to work. Demonstrates excellent ability and knowledge of database principles, data profiling, statistics and insurance-related data modeling and can apply this knowledge in the most complex situations. Develops new approaches, methods or policies in the area. Is recognized as an expert, internally and/or externally.