Senior Modeling & Optimization Engineer

About Mytra: We’re creating an entirely new way to solve the most ubiquitous problem in industry - moving and storing material. We’re applying robotics and distributed software to create a new class of product for this $1T market. We’re focused on the supply chain industry first. The industry is in a massive bind with the continued growth of e-commerce, sharp rise in costs, and supply chain disruptions. What has been a “sleepy” industry for decades is now at the epicenter of sustaining the global economy. About the role You'll own analytical delivery on Mytra's customer engagements — using operations research methods (simulation, optimization, and data science) to solve customer design problems and validate warehouse automation solutions. This is a delivery-owner role, not a support seat: you'll take engagements end-to-end — analyzing operational data, modeling system behavior, running design experiments, and translating findings into recommendations that land deals and shape what Mytra builds next — with the autonomy and judgment to run them without close direction. A defining part of the job is working across two modeling modes and knowing when to use each: fast, low-setup models for rapid design iteration early in a deal, and deeper, higher-fidelity validation when a decision warrants it. Balancing speed against fidelity — and building the tooling that lets others run the fast mode themselves — is central to the role. This role sits within the Modeling & Optimization team, the technical and analytical backbone of Mytra's commercial pipeline. M&O's mandate is to make the commercial org continuously better — and a central way we do that is by building tools that hand capability back to solutions engineers and designers so analytical work isn't gated on our team. You'll both deliver the hard analysis and work yourself out of being the bottleneck on the routine parts. What you’ll do: Own analytical delivery on customer engagements end-to-end — from operational data through to recommendations — with the judgment to scope depth appropriately and run engagements without close direction. Work across two modeling modes: fast, low-setup models for rapid design iteration, and deeper planner-in-the-loop validation for higher-fidelity de-risking — and exercise judgment on when each is warranted. Analyze customer operational data — order profiles, SKU demand patterns, throughput characteristics, and work schedules — to inform system design and sizing decisions. Design, build, and execute simulation models (discrete-event simulation, scoped scenario models) to validate warehouse designs and quantify system performance for customer proposals. Develop optimization models and recommender logic to support layout decisions, fleet sizing, zoning strategies, and other design trade-offs. Build and hand off self-serve tooling to the commercial org — so solutions engineers and designers can run routine analysis themselves and the team is not the bottleneck on every study. Make sense of data across customers and solutions, and help translate what we see on the customer side into product requirements — so this signal reaches product without solutions engineers carrying the full analytical load. Conduct structured experiments, sensitivity analyses, and design space explorations; communicate results and actionable recommendations to technical and non-technical audiences. Build repeatable analytical workflows, scenario configurations, and validation frameworks that raise the baseline capability of the broader commercial engineering org. Ideal Candidates 5+ years of industry or relevant experience. A track record of owning analytical work end-to-end and operating with autonomy — comfortable being accountable for delivery, not just contributing to it. Strong proficiency in Python, including object-oriented design and building production-grade code. Experience with at least two of: discrete-event simulation, mathematical optimization, statistical modeling,