Adela is focused on the detection of cancer and other high-morbidity, high-mortality conditions through a blood test. The Company’s genome-wide methylation analysis technology has the unique ability to enrich for the most highly informative (methylated) regions of the genome from the non-informative regions and preferentially target those informative regions for sequencing. The technology is initially being developed for use across the cancer continuum for detection, diagnosis, and management and in the future will be applied to other conditions beyond cancer.


The Bioinformatics Scientist will lead our machine learning innovation effort using Adela’s proprietary cfMeDIP NGS assay. The assay pulls down from cell-free DNA methylated regions of the human genome using immunoprecipitation. This role will be responsible for developing and refining machine learning algorithms for making cancerfications in the resulting NGS sequencing data. The Data Scientist will also work to establish the methodology for supporting the entire software life cycle of machine learning diagnostic applications in regulated clinical applications in an early start-up environment.


Work with the assay development team in generating protocols for analytical validation studies for regulated submissions

Design, execute and test machine learning algorithms for Adela’s cfMeDIP NGS assay

Work with molecular biologists to optimize wet lab parameters in order to maximize fier performance while controlling costs

Define and maintain the machine learning lifecycle of our portfolio offiers

Design, direct and advise in laying the foundation for a commercial machine learning software commercialization process

Design and implement data visualization for deeper insights

Discover and develop new applications for Adela’s proprietary cfMeDIP assay

As a cloud first company, contribute to a cross-functional ModelOps/MLOps team and establish a framework for bringing ML and AI to production cloud environments

Education & Knowledge:  

PhD in Bioinformatics, Computer Science, Data Science, Computational Biology or related field. Relevant commercial experience can make up for lack of an advanced degree.

A trailblazer that is not afraid to navigate a start-up environment

Understanding of model optimization in complex machine learning models

Experience using machine learning frameworks

Experience developing commercial omics assays in diagnostics; NGS and/or methylome are a plus

The ability to advise and execute on establishing best practices in commercializing ML and AI software applications

Experience submitting ML or AI diagnostic software applications to a regulated body is a plus

A methodical data driven approach that prioritizes reproducibility and documentation

An insatiable curiosity and healthy dose of skepticism that drives critical thinking