Data Scientist

Responsibilities

• 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

Required experience

• Undergrad in quantitative discipline (e.g. analytics, actuarial, mathematics, statistics, engineering, economics).


• 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)