Developing and delivering consumer lines’ pricing & claims strategy by launching price experiments, monitoring them and using optimization techniques (including predictive modeling) to set prices that maximize the value generated through customer acquisition and retention.
Execute all aspects of analytics initiatives including exploratory data analysis, launching experiments, model development, model evaluation and benefit estimation:
Developing queries to extract modelling data from our warehouses, creative data exploration and feature engineering.
Building, enhancing and assembling machine learning and statistical models to predict losses and customer behavior to improve our pricing.
Using and developing pricing algorithms to deliver new price optimizations and tests.
Research, recommend, and implement statistical and other mathematical methodologies appropriate for the given model or analysis.
Create excellent working relationships with business partners across the Chubb organization, IT and analytics peer groups.
Have displayed strong preference to work with business users to make use of data to add value to the day-to-day business.
Have a strong business sense especially when it comes to knowing how to translate technical concepts into language easily understood by lay audience + aptitude in building slide decks
Know how to build rapport with business users to quickly build trust and gain buy-in
3+ years work experience in an analytical, modelling or data science role with programming experience in Python required.
SQL and experience working with large or complex datasets.
Extensive experience of multiple statistical methods, tools and language e.g Python, R, etc.
Hands-on experience with building and developing GLMs and machine learning models.
Experience with version control, automation tools and ML Ops based deployment.
Be a fast learner to understand our business, its value drivers and the data it generates.
Have a customer focus to both spot profit opportunities and do the right thing.
Think creatively to master and combine machine learning and statistical approaches.
Document your work, build pipelines to automate and teach others how to maintain it.
Desired Experience:
Experience in insurance, or customer-facing industries (financial, competitive subscription-based industries like cell phone, ISP, cable) is preferred.
Insurance industry/actuarial experience
Experience with Spark and knowledge of advanced machine learning techniques and frameworks (e.g. H2O)
