Design, implement, and maintain software systems and data pipelines supporting real‑world cancer genomics, medical imaging, and EHR data
Collaborate with interdisciplinary teams to translate analytical and research requirements into reliable, production-quality software
Develop tools and services for data ingestion, validation, transformation, storage, and access, ensuring performance and data integrity
Implement and carry out standard methodologies in software engineering, including version control, testing, documentation
Optimize systems for performance, scalability, and reliability across large and heterogeneous datasets
Communicate technical designs, trade-offs, and implementation details clearly to both technical and scientific collaborators
Contribute to open-source or internal software libraries, platform documentation, and long-term architectural planning

Requirements

PhD in a computational science discipline or master’s degree with 2+ years of relevant experience
Experience working with statistical and machine learning methods applied to genomics and/or imaging data
Familiarity with data storage and management systems, including SQL and NoSQL databases, object storage, data lakes, and data warehouses
Familiarity with common genomics and imaging data formats and standards (e.g., BAM, VCF, DICOM, OME‑TIFF)
Proficiency in one or more programming languages commonly used in data‑intensive systems (e.g., Python, R)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

software designdata pipelinesdata ingestiondata validationdata transformationdata storagedata accessversion controltestingdocumentation

Soft Skills

collaborationcommunicationproblem-solvinganalytical thinking

Certifications

PhD in computational sciencemaster's degree