He works best where analysis and implementation stay close together: forecasting, reporting, modeling, internal tools, workflow automation, and AI-supported operational systems that need to hold up in the real world.
10,000+ hours of programming experience across analytics, automation, reporting, and hands-on systems work.
Forecasting, predictive modeling, workflow automation, internal tools, reporting systems, and operational decision support.
Retail, financial services, and collision-repair operations, with a bias toward messy environments that need practical structure.
Clean, reconcile, reshape, and summarize messy real-world data so teams can actually trust and use it.
Build repeatable reporting workflows that cut manual effort and give operators and leaders timely visibility.
Replace repetitive manual steps with scripts, pipelines, and lightweight systems that reduce friction and mistakes.
Use practical programming to extract, transform, analyze, and operationalize data in real business environments.
Build forecasting processes and models that support planning, tracking, and better day-to-day decision-making.
Apply analytical and statistical modeling when it improves targeting, risk evaluation, or operating outcomes.
Turn analysis into tools, outputs, and workflows that real operators, managers, and executives can act on.
Use AI where it improves intake, triage, or operational throughput, not just because it sounds current.
The point is not just analysis in the abstract. It is building systems, workflows, and outputs that help people make decisions, move work forward, and reduce manual drag.
JCPenney
Ecommerce analytics and forecasting
Built forecasting workflows for ecommerce leadership during high-variance retail periods where planning quality and timing both mattered.
Helped decision-makers move faster with a more structured view of demand instead of leaning on one-off manual analysis.
CoreLogic
Regulated data and model implementation
Worked on production credit models in a regulated environment where analytical rigor and practical implementation both had to hold up.
Supported modeling work that needed to be reliable, defensible, and usable inside a real production context.
Collision Champs
Systems integration, CRM ownership, workflow design, and applied AI
Aaron helped replace a patchwork process spread across BodyShop Booster, handwritten notes, a failed call-center layer, and improvised CCC syncing with a more durable operating system for intake, estimating, customer communication, reporting, and sales follow-up.
The result was a workflow the sales team could actually run on: cloud-based where it mattered, resilient when photos or AI failed, and far less dependent on manual rescue work.
GoHighLevel, estimating, and CCC in one flow
Built a CCC data-sync process from scratch using website scraping, later moved it to the cloud, and connected that data with GoHighLevel, a ChatGPT-based estimator, customer communication, and reporting so the business could work from one coherent process instead of several disconnected tools.
Photo-quality controls and resilient AI handoffs
Evaluated Tractable AI, added Python-based photo-quality checks and guided upload prompts, and built fallback behavior so poor photos or failed AI runs would still move the lead forward.
Admin support for messy real-world leads
Served as the GoHighLevel administrator and added staff-mode overrides so the sales team could work imperfect leads when VINs, photos, or other inputs were incomplete.
More examples
JCPenney
Reporting automation
Built and improved recurring ecommerce reporting so leadership could get consistent visibility without rebuilding the same outputs every cycle.
Reduced manual reporting effort and tightened the cadence between new data and usable business reporting.
CoreLogic
Disciplined analytical process
Worked with sensitive consumer data in a context where process discipline, repeatability, and sound judgment mattered as much as raw analytical ability.
Kept analytical work grounded in workflows that could stand up to scrutiny instead of living as one-off analysis.
"Many people learn to use tools, but not everyone genuinely loves using computers to get work done."
Aaron is in that second group. He likes programming for its own sake, likes the command line, likes building systems, and likes the feeling of taking something messy and making it coherent. That matters because the work does not stop when the ticket is done. The curiosity stays on.
The result is a working style shaped by long-running personal computing habits: scripting, Linux, self-hosted tools, infrastructure experiments, data workflows, and a strong preference for understanding how the machinery actually works.
Why it matters
Teams benefit when someone does not just tolerate technical tools, but actively likes using them well.
That tends to produce better debugging, better automation, better judgment around tooling, and more patience for the rough edges that real systems always have.
Programming is not just something Aaron learned for work. It is a durable habit, a preferred way of thinking, and a toolset he keeps returning to because he genuinely enjoys it.
Self-hosted systems, Linux workflows, scripting, automation, data tooling, and long-running technical curiosity are part of the same continuum rather than separate hobbies.
That experience shows up in the way Aaron approaches systems: practical, hands-on, and comfortable moving from messy inputs to durable outputs.
He does his best work on messy, real business problems where analysis and implementation stay close together. The goal is not theory for its own sake. The goal is useful systems, cleaner decisions, and less waste in the workflow.
Keep analysis close to implementation so ideas do not die in handoff.
Prefer durable systems over heroics and repeated manual rescue work.
Translate rough, messy operations into tools people can actually use.
From June 2021 through December 2025, Aaron took a planned career break focused on primary family caregiving, independent research, and continued technical development. During that period he stayed hands-on through self-hosted systems, automation work, and ongoing study across analytics, infrastructure, and digital-asset market structure.
Primarily remote. Open to the right in-person or hybrid opportunity in Parkland / South Florida, Coppell / DFW, and possibly Reno / nearby.
Work involving SQL, Python, bash, forecasting, analytics, workflow automation, internal tools, reporting systems, or decision-support workflows.
Use the live scheduling link to book a short intro with Aaron at a time that works for you.
Book a 15 min introThis assistant answers questions about Aaron based on his resume and project summaries.