Team lead building telemedicine software in a fast paced environment using Python, React, Redux, XState, PSQL, and MongoDB. Observability and monitoring through Datadog.

Led high-impact patient experiences, including cancellation, intake, and medication adjustment flows.

Launched Self-Cancel Alternatives for weight loss, reducing churn with 200+ daily snoozes, 22% CVR, and a 6.8% drop in D7 cancels.

Delivered Weight Loss Medication Adjustment feature enabling 425+ monthly self-serve changes; reduced manual provider load and cleared $1M in excess inventory.

Architected frontend logic with XState, GraphQL, and custom intake rendering systems.

Built an internal “Intake Speedrunner” Tool, reducing QA time and human error in 20+ question medical flows.

Shipped Hims’ first cross-category ED + Hair promo, integrating conditional flows and interstitials; increased AOV by 18% and boosted retention by 2%.

Planned and shipped React features while coordinating with designers, product managers, and QA.

Studied distributed systems, Golang, and concurrency at self-directed programming sabbatical.

Implemented a distributed file system, consensus module, and membership system, while also blogging about their mechanics and the process of implementing them.

Created blog and self-updating web gallery (chronicpizza.net, knoppers.icu) to host writing and photography.

Presented project progress and interesting findings to peers at weekly talks.

Building and optimizing RESTful Python-flask API endpoints with a focus on unit testing and query efficiency, with Postgres as a persistence layer

Simultaneous development of frontend Typescript-React features and backend Python microservices

Migration of existing features from legacy codebase into new microservice environment

Converted EPA developed stormwater modeling decision tool from Excel VBA to C#, decreasing run time by over 93%.

Documented and implemented this newly revised model into C# Silverlight application.

Implemented web caching for C#-Silverlight application

Created highly configurable and flexible Python scripts for trend analysis of arbitrarily large time-series datasets.