At Target, I lead the strategy, design, and expansion of Nemo, an end-to-end simulation platform for order allocation and available-to-promise that supports scenario testing and policy design across digital fulfilment. I also built and deployed Nemo AI, a production multi-agent assistant for simulation research, diagnosis, and debugging.
I lead a five-person data-science team and partner with product, engineering, supply chain, and operations to turn ambiguous problems into production-ready solutions. My work includes a consolidation-algorithm revamp projected to deliver $20M+ in shipping-cost savings and $300M+ in incremental sales, backed by deep experience in Python, R, distributed data processing, machine learning, algorithms, and software engineering.
