Job Description Help for Partial Job Description. Opens a new window. Data Science Lead Data Science Lead The Data Science Lead will be a lead member of the AGRC North America Data & Analytics (D&A) team, driving the evolution of our advanced analytics and AI capabilities. This role is focused on designing, operationalizing, and scaling analytics, machine learning, and AI solutions that power Risk Consulting engagements. The Data Science Lead will build robust data pipelines and models, create reusable analytics assets, and enable consultants and analysts with tools and frameworks that elevate the quality, speed, and consistency of analytics across the business. This role requires a blend of deep technical expertise, strong business understanding, and a passion for innovation, automation, and continuous improvement. Aon is in the business of better decisions At Aon, we shape decisions for the better to protect and enrich the lives of people around the world. As an organization, we are united through trust as one inclusive team and we are passionate about helping our colleagues and clients succeed. What the day will look like

• Lead Advanced Analytics & AI Development Coordinate and contribute to the design and execution of advanced analytics, machine learning, and AI solutions using claims, exposure, operational, and external data to generate high-value insights for use across Risk Consulting.
• Operationalize Scalable Data & Analytics Foundations Build, maintain, and optimize scalable data pipelines, feature stores, and reporting frameworks using tools such as SQL, Python, Alteryx, Databricks, and Power BI/Tableau to support repeatable, production-grade analytics.
• Develop Reusable Analytics Assets Design and productionize reusable analytics components (e.g., models, data products, templates, and automated workflows) that can be embedded into multiple client engagements and solutions.
• Data Quality, Governance, and Automation Drive innovation in data management by profiling, cleansing, and integrating disparate data sources; define and automate data quality rules; and contribute to proactive data governance and stewardship practices.
• Continuous Improvement & Technology Adoption Evaluate and introduce emerging analytics, AI, and automation tools and techniques to continuously enhance the team's capabilities and efficiency.
• Mentorship & Best Practice Development Mentor junior analysts and data professionals, promote coding and analytics best practices, and help shape standards for model development, documentation, testing, and deployment.