Hi 👋 I'm Jackson.

At Amazon Prime Air, I joined a cross-functional effort to enable drones to perform their own maintenance (software updates, log offloads, sensor calibrations, etc.). The project involved project managers focused on deployment timelines, hardware engineers concerned with drone safety, and technicians who would support the system in the field. The original technical plan would take 7 months, which didn't align with the business need.

I was brought in to find a faster path. After analyzing the architecture, I saw we could simplify the approach and deliver the core functionality in 2 months, with a phased rollout of the remaining functionality later. Despite being dramatically faster than our previous plan, this approach required prioritizing some features over others, and I had to get stakeholders who spoke different languages and had different priorities to buy-in.

I tailored my communication to each group. For product managers, I explained how the cost savings of shipping core features faster outweighed the downside of releasing features piecemeal. For hardware engineers, I walked through technical trade-offs and why the simplified architecture was just as robust. For technicians, I focused on operational simplicity and reduced maintenance burden. Rather than pushing one "right answer," I made sure each team understood how the decision affected their world.

The result was full alignment across teams despite competing concerns. We shipped on time in 2 months, unblocking the broader deployment timeline and enabling autonomous drone maintenance.

As a product management intern at IDeaS Revenue Solutions, a demand forecasting platform for hotels, I investigated years-old client feedback claiming that our software couldn't price meal plan add-ons effectively.

Initial research suggested this was a non-issue. We had features for pricing add-ons, and no one had heard similar complaints. The prevailing theory was user error.

I wasn't satisfied with this answer, so I mapped the complete user journey for setting up add-ons across different customer types. That's when I discovered the gap: hotels and all-inclusive resorts have vastly different revenue models, and we had built optimizations for each—but resorts with "all-inclusive optional" systems (common in EMEA) didn't fit either model. These customers were slipping through the cracks.

I drafted a proposal for a third optimization strategy tailored to these hybrid resorts. The research revealed this would unlock 36% of the world's resorts we were currently unable to serve well, including a major enterprise client we were actively pitching.

At Amazon Prime Air, one of our top organizational goals was reducing drone downtime between flights to under three minutes. Drones represent massive fixed-cost investments, so maximizing flight time directly improves unit economics and profitability.

The biggest bottleneck from my team was the post-flight inspection process. Every drone underwent both a manual visual check and an automated telemetry check, and these were tightly coupled–meaning one couldn't start until the other finished. This coupling was killing our cycle time.

I led the project to decouple these checks. This required coordinating changes across multiple microservices and refactoring workflows throughout our codebase.

As a result, average turnaround time was reduced by 25 seconds per flight. This seemingly small improvement had cascading effects—each launch pad could now operate with two drones instead of three (one flying, one on standby that could be inspected simultaneously), improving our capital efficiency by 33%. The change also reduced technician workload by 65 hours per month, freeing them to focus on higher-value maintenance.