The mission of Nightingale is to enable the world’s top researchers to apply machine learning techniques to high-dimensional health data in order to push forward the boundaries of medical science. This approach is called computational medicine.
Nightingale is funded by Schmidt Futures and housed at the University of Chicago, and is focused on unlocking siloed medical data to make it available to the world’s top machine learning and health researchers who can build algorithms to improve clinical care and effectiveness. Long-term, the Nightingale project will create a virtuous cycle, using real clinical outcomes to feed into continued research developments and clinical improvements. This effort will begin by building a platform to host already-collected health data, enable research, and facilitate clinical utilization.
The Director of Engineering will create the infrastructure to allow these open datasets to counter and disrupt the many companies trying to collect, hoard, and privatize healthcare data for profit. The Director of Engineering will be the primary owner of decisions around the data architecture and implementation for the work, including choice of computing frameworks, building robust data infrastructure, and ensuring the highest levels of adherence to security requirements. The Director will work with a small, closely-knit project team, and collaborate with many partners, including leadership at Schmidt Futures and in the Research Computing Center at the University of Chicago, administrators and technical staff at health systems who will be data sharing partners, and others. The ability to effectively manage this cross-functional project team will be a key part of making the technical work successful.
- Design and implement, from the ground up, the schema and system for integrating with various health systems’ data sources.
- Interpret, clean, and transform large scale health data within the context of machine-learning technical requirements to create an easily consumable format.
- Own and manage any de-identification projects to ensure re-identification impossibility.
- Create a user-centered front end for non-technical leaders at the health systems to better use and understand their own data.
- Engage with clinicians, economists, engineers, product managers, designers and data scientists on the project team and at partner organizations to scope technical goals and solutions of projects (e.g., database architecture, API needs, etc.).
- Manage execution and timely completion of engineering components of projects.
- Identify and develop strategy to overcome technical risks (e.g., data security, de-identification, etc.).
Technical partner management and alignment
- Identify skill-sets required for project success and recruit individuals with those skills to project teams.
- Oversee engineers and data scientists at partners to ensure alignment of solutions built with project goals and alignment across partner organizations.
- Identify and engage experts to help answer specialist technical questions.
- Have or gain a detailed understanding of HIPAA compliance as it relates to the database and access to it.
Knowledge, Abilities, and Skills
- Engineering degree (or equivalent level of training and expertise) in Computer Science
- 8+ years experience in Software Engineering or Data Engineering
- Experience with data modeling and schema design in a database or cloud based data warehouse
- Experience with de-identification and re-identification processes and risks
- Experience working with large data sets
- Knowledge of high dimensional data modeling, machine learning, and hands-on experience in applying ML to solve real-world problems in non-Computer Science fields preferred
- Emotional intelligence with the ability to listen, build relationships, and draw out expertise and support from colleagues, advisors and partners
The team is remote and candidates can be located anywhere within the U.S. Some travel will be required.