Decision Scientist Analyst

Berkley•Irving, TX

25d•Hybrid

About The Position

Berkley Entertainment Insurance is seeking a Decision Science Analyst to join the team in their Irving Texas headquarters (hybrid office arrangement). This mid-level role will focus on transforming raw data into actionable insights that drive strategic decisions across the organization. The analyst will work closely with cross-functional teams including Underwriting, Claims, Finance, and Actuarial to support data-driven initiatives and improve operational outcomes.

Requirements

3-5 years of experience in data analytics, decision science, or a related field.
Proficiency in data cleaning and transformation techniques.
Strong experience with Power BI , Azure AI , and Databricks .
Familiarity with LLM integration and prompt engineering.
Solid understanding of statistical analysis and data visualization.
Proficiency in SQL, and Python or R for advanced analytics .
Excellent communication and storytelling skills using data.
Bachelor's degree with emphasis in Computer Science, Computer Information Systems or equivalent work experience

Nice To Haves

Exposure to RAG / MCP, preferred
Experience with machine learning models , including training, validation, and deployment, preferred
Exposure to MLOps and model lifecycle management, preferred

Responsibilities

Clean, transform, and analyze large datasets to support business decision-making.
Develop and maintain dashboards and reports using Power BI .
Leverage Azure AI and Databricks to build scalable data pipelines and analytical models.
Collaborate with business units and IT teams to integrate Large Language Models (LLMs) into workflows.
Translate complex data findings into clear, actionable insights for stakeholders across underwriting, claims, finance, and actuarial departments.
Support experimentation and model development for predictive analytics and machine learning initiatives.
Document processes, methodologies, and findings for transparency and reproducibility.
Perform other duties as assigned