• Built and maintained an internal patient management platform that was used by our staff of care partners and nurses to provide care navigation and support to thousands of cancer patients

• Led technical design, implementation, quality testing, monitoring and deployment of new platform features. Collaborated across multiple teams and functions - including engineering, data science, product, design, legal and ops - to ensure smooth feature rollout with zero downtime and minimal disruption to user workflows.

• Proposed and implemented a major version upgrade to a Python serialization library that touched almost every file in our 250K line codebase and reduced API serialization times by an average of 60%

• Led design, implementation and rollout of a system to send and receive signed patient consents in our internal platform, simplifying this workflow for our users and reducing context switching and cognitive load on our care. Built with Vue.js, backed by an async event pipeline using Amazon SQS and S3, and integrated with the DocuSign API.

• Streamlined developer workflows by refactoring our Python code linting and formatting pipeline, reducing lint times by over 90%

• Pioneered and standardized the use of Datadog to collect app usage statistics, enabling product and operations teams to monitor user behavior in real time and make data-driven product decisions

• Led multiple incident responses, driving root causing and resolution across multiple teams, communicating incident status to affected users in real time, and moderating incident post-mortems.

• Developed and maintained an in-house electronic health records (EHR) application used at over 100 nationwide primary and urgent care clinics

• Redesigned the EHR's appointment scheduling system, used daily by clinic staff and tens of thousands of patients, to use a dynamic time slot model that allowed providers to be scheduled down to the minute. Led implementation and rollout of the new scheduling system to all of our clinics with zero downtime, unlocking higher levels of clinic/provider efficiency

• Migrated user authorization and appointment scheduling backend functionality from a legacy Scala monolith to Kotlin microservices deployed independently on AWS, allowing the services to scale independently with demand and enabling a 3x increase in overall API request rate

• Built and deployed inferencing pipelines and APIs that provided ML disease detection and image quality assessment for daily workloads of thousands of ultrasound videos. Backends and APIs written with Python / Flask and deployed on Kubernetes in GCP

• Drove testing, QA and external deployment and release of inferencing APIs, providing ultrasound labs with valuable clinical insights and helping Caption Health secure revenue and contract success

• Collaborated across engineering, data science and product teams on API design, scoping and planning, and engaged with external customers to gather product feedback.

Built an centralized platform that enables data scientists across GE Healthcare modalities to develop, train and deploy machine learning models at scale.

Technologies: Python, Flask, Java/Spring, AWS (SageMaker, S3, ECS, Lambda)