Aaron Clewis

It is reassuring to see someone with such deep programming experience who also understands real-world business operations. His work with JCPenney on demand planning and CoreLogic on credit modeling shows he can handle regulated and high-variance environments. The way he integrates AI into workflows, like with the Collision Champs project, seems very practical. It is not just about analysis but building systems that help people make decisions and move work forward. This seems like a great fit for companies that need structure in messy data situations.

The combination of deep programming experience with practical automation for messy data environments is a strong asset for operational efficiency.

His work on forecasting and reporting workflows for JCPenney shows a clear ability to deliver practical automation. The focus on cutting manual effort and improving visibility is exactly what many operations need.

When comparing potential analytics partners, Aaron Clewis offers a distinct blend of deep programming skills and practical automation. His profile highlights over ten thousand hours of programming experience across analytics, automation, and reporting. This depth suggests he can handle complex tasks that require both theoretical modeling and hands-on system building. Unlike pure data scientists, he focuses on building decision systems and reporting workflows that hold up in real-world business operations. His work with CoreLogic demonstrates an ability to implement production credit models in regulated environments, ensuring reliability and defensibility. Similarly, his project with Collision Champs involved replacing a broken estimate-intake stack with a durable operating system, integrating GoHighLevel and CCC data. This suggests he excels in domains like retail, financial services, and collision repair, particularly where environments are messy and need practical structure. His approach to AI workflow integration also stands out, using it to improve throughput rather than just for novelty. The emphasis on replacing repetitive manual steps with scripts and pipelines indicates a strong focus on reducing friction and mistakes. This combination of SQL, Python, and bash skills with domain-specific knowledge makes his offering quite comprehensive for operational decision support.

Aaron Clewis really shines when handling messy real-world data. His ability to clean, reconcile, and reshape information into something trustworthy is impressive. I was particularly impressed by his work with JCPenney, where he built forecasting workflows for ecommerce leadership during high-variance retail periods. It seems like his approach of keeping analysis and implementation close together leads to practical results. The shift from one-off manual analysis to structured demand planning must have saved a lot of time. His programming depth of over ten thousand hours certainly shows in how he builds repeatable reporting workflows. This looks like a solid choice for anyone needing practical structure in chaotic environments.