The Global Analytics Team at Chubb is seeking a data scientist with 3+ years of industry experience to join our fast-paced, high energy team. This team is responsible for building data pipelines and delivering predictive modeling solutions to our business partners that will meet business objectives and move-the-needle to improve upon key performance metrics.
Job Summary
As a data scientist for the Global Analytics Team, you will develop predictive modeling / machine learning solutions to complex business problems and create value to the business. This position offers exposure to a wide variety of analytic tools and technologies as well as unique challenges in problem-solving. Be ready to leverage internal/external data sources as you develop, deploy and monitor best-in-class model solutions.
Primary Responsibilities
Collaborate with business partners and peers in the organization to understand and scope the problem, gather business requirements and develop robust model solutions that drive improvement in key business metrics.
Execute all aspects of analytics initiatives including exploratory data analysis, data preprocessing, model development, model deployment and monitoring.
Ability to synthesize data, to uncover inherent trends in data, to assess impact of data on business usage, and to make recommendations for improvement.
Thoughtfully identify and construct predictive variables from both internal and external data sources.
Research, recommend, and implement statistical and other mathematical methodologies appropriate for the given business problem.
Create excellent working relationships with business partners across the Chubb organization, IT and analytics peer groups.
Provide supporting documentation for the models developed including documentation of methodologies used, data issues encountered, and responses to regulatory requests.
Effective communication to key stakeholders in written, oral and presentation formats.
Mentor junior data scientists.
Required Skills/Experience
Excellent understanding of data mining, predictive modeling and data visualization.
Hands on experience utilizing both supervised and unsupervised ML algorithms.
Advanced knowledge of model tuning, evaluation and operationalization.
Significant programming experience in both SQL and Python. Experience with SAS, R, H2O is a plus.
Text Analytics, Natural Language Processing experience is preferred.
Working knowledge/familiarity with git is preferred.
Experience in personal lines insurance is a plus.
Hands-on experience with emerging big data technologies (Azure Cosmos DB, Databricks, Spark) is a plus.
The ability to multi-task, learn new things quickly, excellent problem solving & communication skills a must.
Collaboration, knowledge sharing and keeping up to date on emerging industry trends is a must.
Education
If the candidate’s background is Actuarial science, an ideal candidate would have a solid understanding of advanced actuarial techniques and statistical modeling with at least 5 years of experience and 5+ exams completed (ACAS preferred).
If the candidate does not have a background in Actuarial science, the candidate should have 3+ years of industry experience building and analyzing predictive models and a graduate degree in a technical field such as statistics or computational science.
