Senior Insurance Data Analyst

Orion180•Irving, TX

3h•Onsite

About The Position

We believe analytics isn't a back-office function. It's not a reporting layer bolted onto the business after the real decisions get made. It's the foundation that tells you where risk actually lives, where growth is hiding, and where you're missing opportunities before it's too late to do anything about it. That belief is driving real investment in our people, platforms, and in how deeply analytics is woven into the way we operate. From underwriting to distribution to customer experience, we're building a data culture where insight drives action. It also means evolving how we think about the role of an analyst. The expectation isn’t just technical execution. It’s curiosity. Ownership. The ability to step into ambiguity, frame the problem, and help guide the business toward better decisions.

Requirements

Bachelor’s degree in Statistics, Data Science, Computer Science, Mathematics, or a related field
Proven experience in data analysis, modeling, and visualization within the insurance or financial services industry.
Proficiency in Python, SQL optimization, statistical modeling, API development, and forecasting techniques.
Hands-on experience with cloud platforms such as AWS, Azure, or GCP and familiarity with MLOps frameworks.
Strong understanding of both data science and actuarial principles.
Excellent analytical, problem-solving, and critical-thinking skills.
Strong communication and collaboration abilities across technical and non-technical teams.
Ability to manage multiple projects in a fast-paced, results-driven environment.
Proper work authorizations to work in the US without sponsorship.

Nice To Haves

Experience or familiarity with semantic data modeling and governed metric layers.
Working within scalable cloud data platforms (e.g., Snowflake, Databricks).
Integrating analytics within broader data engineering workflows.
Exposure to emerging capabilities such as AI-assisted analysis, automated insight generation, and advanced data visualization techniques.

Responsibilities

Develop and maintain reproducible, scalable data pipelines to support advanced modeling and analytics workflows.
Design and implement predictive and time-series models using classical and machine learning methods.
Collaborate with sales, underwriting, claims, and actuarial teams to integrate models into production environments.
Leverage Python, SQL, and statistical analysis to identify key data patterns and multivariate correlations.
Conduct exploratory data analysis and ensure data integrity across structured and unstructured datasets.
Document model assumptions, governance processes, and validation results to maintain compliance with regulatory standards.
Build intuitive dashboards and data visualizations using Power BI that translate complex insights into clear business intelligence.
Communicate findings and recommendations to cross-functional teams, including leadership and technical partners.
Stay current with industry trends and emerging data science tools to continuously improve analytical capabilities.