- My role is to act as a specialist solutions architect with the AWS Device Farm service, serving as an expert on the service to our customers, as well as on our customers to the service team. - I work hands-on with the largest customers who evaluate our service, enabling them to onboard with as little friction as possible, while also conveying (and occasionally correcting) whatever bugs they may face in the process. - Through my customer-facing exposure, I inform our product team on the importance of different features and the best technical direction for customer satisfaction. - Similarly, I've trained the service team over the years on how to deal with customer issues most effectively. - This role is cross-functional and full-stack: I ensure my customers are successful along every step of their end-to-end journey, and resolve whatever technical blockers they face in the process. In order to meet the needs of my customers most effectively, I've become an expert at every stage of the service in terms of its infrastructure and software. - Whether it means ordering devices myself for the data center, developing tools to get them deployed properly, developing monitoring systems to ensure that they're online effectively, developing dashboards to assess customer experience, or developing integration code directly for customers, there are no bounds for where I go to guarentee customer success across every technical dimension.

Designed and implemented a system for functional testing, continuous integration, and deployment for iOS apps on real devices. Enhanced our STF-based android testing system through validation testing and healing services. These services ensure that at any given point, our android devices and their providers are always operating properly. If a heal cannot fix whatever problem is going on, we receive an alert in slack with information about the problem so we can manually asses the situation. These tools and services include a flask-based dashboard for assigning performance tests to devices, webhooks and integrations communicating from Grafana's alerts to the dashboard for healing to slack for output, and many others running as systemd-initiated Docker containers. Scripted the provisioning and configuration of general Android devices using Python's uiautomator and Android's ADB frameworks so that they can be integrated into our STF-based device farm seamlessly.

Performed predictive analytics on customer data attributes using Python and its scikit-learn library. Implemented a data cleansing and validation mechanism to make unstructured JSON data into feature vectors in a way that scales for 168,000 unique feature-value pairs to be persisted to disk for 118 million customers. Tuned and automated training on predictive models using these feature vectors. Implemented a prediction verification mechanism to determine the accuracy of the predictions over time.

Work study student working as a data analyst with the university's databases. I designed queries for various academic departments using Oracle's PeopleSoft.