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.

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.

Skills and experience that will lead to success:

Analytical & Technical Skills

Advanced analytical capabilities with a proven ability to interpret complex datasets, identify trends, and develop robust statistical, ML, and AI-driven solutions. Expertise in data profiling, cleansing, and transformation, including scripting and automation of data quality rules. Strong proficiency in managing relational databases, cloud-based data environments, and ETL/ELT processes.

Tooling & Platforms

Hands-on experience with SQL, Python, Alteryx, Power BI/Tableau, and Excel/VBA. Familiarity with Databricks and cloud platforms (Azure, AWS, or GCP). Experience working with Access and other legacy systems is a plus.

Communication & Collaboration

Ability to translate complex technical concepts into clear, business-relevant explanations for non-technical stakeholders. Strong collaboration skills, working effectively with consultants, product owners, and other technical teams.

Work Environment & Mindset

Comfortable operating in a fast-paced, performance-driven environment. Demonstrates a growth mindset, curiosity about new tools and methods, and a passion for innovation and continuous learning.

Education

Bachelor's degree in a quantitative field such as Mathematics, Statistics, Economics, Computer Science, Data Science, or Information Management 5–7 years of progressive experience in data analytics, business intelligence, or data science, ideally within insurance, risk management, or consulting environments. Demonstrated expertise in data modeling, statistical analysis, and predictive analytics, with hands-on experience using tools such as SQL, Python, R, and Alteryx. Proficiency in data visualization platforms (e.g., Power BI, Tableau) and experience designing client-facing dashboards and reports. Strong understanding of ETL frameworks, data warehousing concepts, and cloud-based data ecosystems (e.g., Azure, AWS, GCP, Databricks). Experience working with large, complex datasets, including structured and unstructured data from multiple sources