Actuary/Data Scientist

Company Details

"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.

The Company is an equal employment opportunity employer.

Responsibilities

The Actuary/Data Scientist will leverage strong analytical and coding skills to support data pipelines and advanced modeling, emphasizing rigorous quantitative thinking, high quality code, and sound professional judgment. This role will challenge the status quo, apply modern data science and AI approaches, and translate complex analyses into clear, actionable insights.

• Write production-quality code for data wrangling, modeling, and AI-assisted analytical workflows.
• Perform deep exploratory analysis to identify problems, trends, and drivers.
• Work 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.

Qualifications

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, with 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).

Education Requirement

• Master's degree in Data Science preferred.
• Progress toward CAS credentials (ACAS or nearly/newly FCAS or international equivalent).

Additional Requirements

Sponsorship Details