Machine Learning Modeling Engineer

We are seeking a talented Machine Learning Data Engineer with a solid software engineering background and experience in building and understanding predictive models. The ideal candidate will bring expertise in the P&C insurance industry and possess proficiency in SQL, Azure, and Databricks. Experience with Generalized Linear Models (GLM) is a valuable asset.

• Develop and implement machine learning models and data solutions to support insurance operations.
• Collaborate with data scientists, software engineers, and other stakeholders to ensure the successful deployment of models
• Manage and process data within cloud environments, primarily using Azure services.
• Utilize Databricks for data processing and model deployment.
• Develop and maintain data pipelines and data integration solutions.
• Ensure scalability and reliability of machine learning solutions.
• Apply Generalized Linear Models (GLM) techniques to enhance predictive modeling and pricing strategies.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Strong software engineering background with proficiency in programming languages such as Python, Java, or C++.
• Solid understanding of machine learning concepts with hands‑on experience in building and deploying models.
• Extensive experience in SQL and data manipulation.
• Hands‑on experience with Azure services, including Azure Data Factory.
• Proficiency in Databricks for data engineering and model deployment.
• Knowledge of various databases and their implementation in cloud environments.
• Experience in the Property & Casualty (P&C) insurance industry is highly preferred.

• Experience with Generalized Linear Models (GLM).
• Previous experience in the Property & Casualty (P&C) insurance industry.
• Experience with large‑scale data processing and integration.
• Strong problem‑solving skills with the ability to work collaboratively in a team environment.

Any relevant certifications in Machine Learning, Azure, or Data Engineering (optional).