Digital & Computational Pathology Lead

Team Description:

The Pathology Department is responsible for the macroscopic and microscopic evaluation of tissue samples from our study participants. At present, there are four cores of the Pathology Dept, which include Anatomic Pathology, Histology, Digital/Computational Pathology, and Lab Operations.

Job Responsibilities:

In this role, you will be supporting the computational pathology team in hypothesis testing, statistical analysis, data visualization, and building AI models. You will have a unique chance to develop cutting-edge infrastructure and automation tools for digital pathology workflows

• Assist digital pathology scientists with hypothesis testing and statistical analysis of high-dimensional data
• Create visualizations that help understand and interpret the complex high-dimensional data
• Work closely with scientists and collaborators to develop AI models for complex computational pathology tasks
• Select data for training/testing of the AI models and evaluate the performance of the models
• Assist in architecting, developing, and validating digital pathology infrastructure solutions for large file management and seamless data flow
• Lead data management and tracking activities such as receiving images and metadata from different sources, quality checks, organization, and reconciliation activities
• Lead workflow optimization and automation efforts
• Effectively communicate and present learnings to a diverse audience
• Train fellow scientists on the new digital pathology and workflow automation tools and write SOPs

Key Qualifications:

• BS in computer science, electrical engineering, biomedical engineering, bioinformatics, mathematics, physics, or equivalent education with 7 or more years of experience or MS or with 5 or more years of experience
• Strong Python scripting experience in writing clean and scalable code 
• Experience with Python packages such as pandas and numpy along with Python statistical packages such scipy or R 
• Experience with data visualization software such as JMP, and PRISM; data visualization Python packages such as seaborn, matplotlib, etc. 
• Understanding of statistical significance tests

Preferred Qualifications:

• Understanding of dimensionality reduction algorithms such as PCA, LCA, SVD, UMAP, t-SNE, etc. 
• Experience with digital pathology image analysis software such as Halo, Visiopharm, QuPath, etc. 
• Experience with AWS - S3 buckets, EC2 instances and other high-computing platforms