Garner Health

We have four major technology teams at Garner:

The Data Science team develops a Python-based metaprogramming system built on novel statistical algorithms to identify fundamental components of health economics in raw data. Their focus is to leverage this system to produce best-in-class metrics for individual doctors.

The Product Engineering team owns and deploys our customer-facing React apps and the underlying NodeJS services supporting them. Their focus is to tailor Garner’s data in real time to be relevant and compelling for the specific member using the app.

The Data Engineering team manages the data flow, quality, and warehousing of Garner’s synthesized, acquired, and proprietary datasets. Their focus is to ensure Garner has a clear and consistent picture of the data we interact with as well as the means to create derived data.

The Platform Engineering team works to create a productive and simple space on which other teams can deploy software. This includes provisioning Garner’s AWS infrastructure, managing Kubernetes, and setting up security and monitoring tooling so we can take proactive steps to maintain system reliability.

Each team works on thematically consistent stories tracked in GitLab following a lightweight agile process. We work together closely, whether in office hours, on Slack, in code reviews, or pair programming when things get tricky. We release new features every 4–6 weeks and bug fixes as needed.

We have assembled the largest healthcare claims dataset in the United States, containing 61 billion insurance records from 135 million patients that document patient journeys across every doctor in the nation. One of our major challenges is to identify high quality doctors with a clear, objective, and indisputable methodology. This involves building infrastructure that can process an enormous volume of data efficiently and developing algorithms that allow health economists to encode evidence-based best practices into working software.

With our growing and deepening understanding of doctor quality, the next challenge we face is to deliver care recommendations that are personalized to a specific member’s needs while taking into account their location, insurance network, and the availability of the doctor. We deliver those recommendations across iOS, Android, and web with a single codebase. Then we plug into insurance data feeds to identify and reimburse expenses our members incur when they receive care from one of the doctors we recommended to them.

As part of our commitment to an inclusive workplace, we are happy to offer prospective engineers the chance to connect with our engineering employees who come from underrepresented backgrounds. It’s a way to get a better sense of our team and what it might be like to work with us.

If you’re interested in connecting with our team, be sure to bring this up during one of our introductory calls!