New York, New York, United States
Assistant Vice President, Data Science and Analytics in Enterprise Analytics. Lead data and analytical transformation in quantitative marketing, algorithmic underwriting, and AI assist claim processing
- Drive growth and efficiency through AI/ML‑powered solutions that unlock value across Guardian’s business lines, including Group Benefits, Financial Protection, and Wealth Management. - Transform ideas into business impact by partnering closely with business leaders and subject‑matter experts to co‑create innovative, scalable solutions from concept through implementation. - Identify high‑value opportunities and connect disparate initiatives and use cases into cohesive, forward‑looking capabilities that fuel long‑term growth and sustainable operational improvements.
· Led collaborative ideation with business leaders to evaluate current state, data readiness, and co-design solutions to drive adoption across insurance value streams and enable 2030 strategic objectives · Developed and maintained a comprehensive AI roadmap for Group Benefit verticals, aligning near-term and long-term initiatives with capability building, execution priorities, and measurable business impacts · Orchestrated cross-functional teams spanning Data & AI, Business, and Technology to deliver $80M+ in revenue growth and $15M+ in cost savings within three years. · Delivered 10+ production grade Data Science and AI products across Group Benefit value streams. · Fostered trusted partnerships with stakeholders to shape future strategies and operational roadmaps.
Lead a team of data scientists and develop analytic solutions to enhance underwriting, actuarial pricing, claim prediction, and reserving. - Design and build multi-stage model to triage applicants for simplified underwriting and segment risks by leveraging all structured underwriting evidence - Design and build tree based models to adjust rate manual at block level on Group LTD and Group Term Life to achieve better A to E across critical rating factors and reduce cross subsidy - Utilize claim process data to better predict the timing of STD to LTD transit, and probability of recovery from LTD to better align resources with customer needs and improve efficient and customer satisfaction - Refine key actuarial assumptions by utilizing all available data on LTD claims to better predict the timing of social security benefit approval, which enables a more refined model on reserve
Analyze GE aircraft engine leasing business model; build structured econometric model to predict future engine leasing revenue and engine maintenance cost based on economic indicator; create stocastic model to analyse profit/loss and optimize leasing price.