Candor Health is a healthcare intelligence platform that is powering companies with high quality, transparent, and actionable data in the US on doctors, facilities, insurance plans, costs, and more. We believe that providing higher quality, transparent data not only leads to better decisions, but creates greater trust and confidence for the entire healthcare ecosystem. We are getting data from a variety of growing sources including certification boards, booking sites, claims, social media, and others, with human and automated verification. We have built a high quality data set that has been baked off favorably against other healthcare data vendors, including public companies and startups that have been compiling healthcare data for many years. We are backed by some of the most prominent VCs and angels in healthcare who have a track record of building very successful technology companies.
Life at Candor
At Candor, everyone is a leader. The areas you own are yours to build and progress. Best ideas win! We value a culture where the team not only enjoys working together solving some of the biggest data challenges in the healthcare industry, but also has fun doing it. Building a great company is hard work but work-life balance is also important.
Work with leaders to design and solve industry critical problems
Design and build systems from scratch and drive them to completion
Active participation to build and improve Candor’s innovation culture
Create technical documentation for reference and reporting
Work well within teams and effectively collaborate with others
Proactive attitude on raising concerns and providing solutions
Bachelor/Master degree in Engineering, Computer Science or relevant field
Great analytical and communication skills
Experience in Python, Golang, Java or other programming languages
Experience in object-oriented programming and data structure concepts
Good to have exposure to Python and Pandas based data extraction, exploratory data analysis and data cleaning
Good to have exposure or interest in Artificial Intelligence, Machine Learning (ML) models, ML infrastructure, Natural Language Processing or Deep Learning