ActuaryData Scientist

Our Company provides a state of predictability which allows brokers and agents to act with confidence.

Founded in 1967 W. R. Berkley Corporation has grown from a small investment management firm into one of the largest commercial lines property and casualty insurers in the United States.

Along the way weve been listed on the New York Stock Exchange become a Fortune 500 Company joined the S&P 500 and seen our gross written premiums exceed $10 billion.

Today the Berkley brand comprises more than 60 businesses worldwide and is divided into two segments: Insurance and Reinsurance and Monoline Excess. Led by our Executive Chairman founder and largest shareholder William. R. Berkley and our President and Chief Executive Officer W. Robert Berkley Jr. W.R. Berkley Corporation is well-positioned to respond to opportunities for future growth.

The Company is an equal employment opportunity employer.

Job Description:

Key Functions/Duties of Position:

• Write production quality code for data wrangling modeling and AI-assisted analytical workflows.

• Perform deep exploratory analysis to identify problems trends and drivers.

• Work effectively with data platforms and pipelines designed by data engineers to develop enhance and maintain models focusing on analytical correctness and model integrity.

• Design and apply machine learning tools including accessing large language models (LLMs) or building task-specific agents.

• Apply professional skepticism and alternate approaches to thoroughly validate results.

• Communicate results effectively to actuarial peers management and non-technical audiences.

• Understand the different data types and uses of data within an insurance organization.

• Provide support and guidance to others who are at earlier stages in their data science or AI journey.

Education Requirement:

Masters degree in Data Science preferred.

Progress toward CAS credentials (ACASor nearly/newly FCAS or international equivalent).

Qualifications:

4-7 years of relevant actuarial technical or research experience.

Strong programming skills particularly in Python including analytical and modeling libraries.

Experience applying AI machine learning or LLM based tools to solve real data or analytical problems (e.g. building agents calling model APIs or integrating AI into analytical workflows).

Proficient in probability and statistics. Experience working with large and complex data flows and articulating project plans and conclusions.

Proficient with SQL and cloudbased or distributed data environments (e.g. Snowflake Databricks or similar platforms).

Strong professional judgment curiosity and attention to detail.