"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, we’ve 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:

Master's degree in Data Science preferred.

Progress toward CAS credentials (ACAS or 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 cloud‑based or distributed data environments (e.g., Snowflake, Databricks, or similar platforms). 

Strong professional judgment, curiosity, and attention to detail.