Corteva R&D Automation Engineer

Product Strategy: The "Business Thinking"

My role at Corteva wasn't just to "write code" or "design parts"—it was to bridge the gap between leadership, scientists, and engineering. I worked on three distinct projects, but they all shared a common strategy: Velocity through modularity.

Whether it was a stalled hardware project or a blind spot in data collection, my approach was to break complex problems into small, testable steps.

• Velocity over Perfection: On the hardware side, I prioritized rapid iteration (laser cutting) over "perfect" manufacturing methods to prove physics concepts faster.

• Maintainability as a Feature: I chose Django (Python) and standard Git workflows not because they were flashy, but because they integrated seamlessly with the existing engineering team's skillset, ensuring the tools would live on after I left.

The Problem

I tackled three specific bottlenecks that were slowing down R&D operations:

1. The "Intern-Proof" Project (Granule Dispersion Rig): This project had stubbornly stalled for three years. The goal was to observe fertilizer/pesticide granules dissolving in a flowing stream, but turbulence and lighting issues prevented a clear image. Previous attempts failed because the problem wasn't broken down into testable variables.

2. The "Data Silo" (Robotic Migration): During a massive robotic storage migration, leadership was flying blind. The tracking process was entirely paper-based. Scientists wrote numbers on clipboards, manually entered them into Excel, and used calculators for totals. Leadership had no real-time data to make informed decisions.

3. The "Cost Barrier" (Temperature Monitoring): The lab needed widespread temperature sensing, but commercial solutions were cost-prohibitive (~$800 for 4 readings) and rigid, making it difficult to scale data collection across the facility.

The Build

I acted as a full-stack technical partner, handling mechanical design, software architecture, and team culture.

• Cracking the "Intern-Proof" Project:

  • I treated the rig as a physics problem first and a design problem second. I realized we needed to validate the flow physics before finalizing the enclosure.

  • I executed eight mechanical iterations rapidly. I chose laser cutting over 3D printing for the early prototypes. Even though it was technically more challenging to design water-tight seals for flat plates than to outsource to a manufacturing company, it allowed us to iterate daily rather than weekly.

  

  • We tested shape, focal length, and material clarity in isolation, eventually creating a rig that stabilized the granule in the flow for perfect dissolution videos ready for study.

• The Data Historian (Django):

  • I built a Django web app connected to an on-premise centralized database. This replaced the clipboard workflow with a digital scanning system.

  • The app automated searching and totaling, providing leadership with instant dashboards of migration progress without interrupting scientists' workflow.

• IoT Cost Engineering:

  • I engineered a custom sensor using a Raspberry Pi and thermocouples housed in a 3D-printed case.

  

  • I wrote the firmware in Python using custom internal protocols to send data directly to our servers. By avoiding vendor lock-in, I ensured any engineer on the team could maintain it.

• Culture & AI Operations:

  • I noticed communication gaps in the team's coding workflow, so I ran lectures on modern Git collaboration.

  

  • I built custom AI agents to streamline documentation, turning tedious form-filling into a simple conversation with Copilot, keeping engineers focused on building rather than paperwork. But more importantly, leadership finally had the information they needed to make informed decisions that helped the entire team.

The Outcome

By applying a "startup mindset" to corporate R&D, we achieved significant operational improvements:

• Solved the 3-Year Blocker: Delivered the first working prototype for the granule dispersion rig, enabling scientists to finally visualize and record the dissolution process.

• 75% Cost Reduction: My custom IoT device costs <$250 for 8 readings, compared to commercial units costing ~$800 for 4 readings.

• Instant Visibility: The Data Historian transformed the robotic migration from a "black box" into a data-driven operation, allowing leadership to make decisions based on real-time progress.

• Cultural Impact: My work on Git and AI documentation modernized the team's workflow, bridging the communication gap between executive leadership and the engineers on the ground.