Job Description

Role Description

As a member of the Global Analytics team, this position will bring knowledge and expertise in modeling methodologies, dataset generation and transformation, statistical programming, and analysis to Chubb. Opportunities will exist to work in a dynamic environment on a broad spectrum of analytical initiatives that impact underwriting and marketing. The role will be responsible for applying statistical and data mining techniques to identify profitable growth areas and optimize portfolio performance. The person will be expected to understand and analyze insurance risk factors and articulate results to various stakeholders, including but not limited to underwriters, product managers, and actuaries.

Major Duties and Responsibilities

Identify, acquire, evaluate, and document data from various sources, both internal and external.

Extract and manipulate data using Python or other data management tools from internal and external data sources.

Understand and combine data from various sources to create analytics datasets. Develop a strong working knowledge of how current systems and data sources are populated and sourced.

Build predictive models and analytic solutions using GLM, GBM, trees, and other machine learning techniques to draw meaningful conclusions and assist in developing solutions to help drive profitability and/or growth.

Introduce novel methodologies, algorithms, tools, and technologies to solve assigned problems.

Collaborate with Underwriting, Actuarial, and Implementation teams to achieve objectives.

Communicate and present findings to business partners to ensure successful integration of projects into the business process. Proactively follow up on any issues raised during presentations.

Create and maintain documentation associated with models.

Develop cutting-edge and advanced analytics solutions, promoting innovation through continuous learning and staying up to date with industry trends and best practices.

Provide training guidance and assistance to colleagues, ensuring knowledge transfer and development of technical skills.

Qualifications
Desired Qualifications

Degree in Actuarial Studies or related fields such as Computer Science, Statistics, Math, or related quantitative fields.

2+ years of experience in non-life insurance pricing.

Possess good capabilities in data engineering, data transformation, data analysis, and data visualization.

Good knowledge of machine learning concepts and algorithms (e.g., GLM, GBM, Random forest, etc.).

Hands-on experience with Python. Proficiency in other programming languages and actuarial software, such as SAS, R, SQL, Emblem/Radar, is advantageous.

Experience in Databricks, Spark, Github will be a plus.

Superior analytical and problem-solving skills.

Able to complete analytical tasks independently with some guidance, if necessary.

Strong organizational skills, able to work well under deadlines in a changing environment and perform multiple tasks effectively and concurrently. Produces quality work in a timely manner.

Excellent communication and interpersonal skills, including the ability to communicate complex technical issues effectively.

Creative mindset to look beyond articulated problems and offer alternate perspectives.

Willingness to learn and adapt to new technologies.