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In this role, you will work closely with Biostatistics faculty and scientific teams whose research directly informs clinical decision-making and improves patient outcomes.

Together, we are building robust, production-quality computational systems that enable advanced analysis of complex biomedical datasets. This role is ideal for someone who thrives at the intersection of statistical modeling, machine learning, and scalable software systems, and enjoys translating complex scientific questions into reliable, maintainable, and reproducible solutions

• Design, implement, and maintain algorithms, software systems, and data pipelines supporting cancer genomics, medical imaging, and clinical data
• Collaborate with interdisciplinary teams to translate research and analytical questions into production-quality computational solutions
• Develop tools and services for data ingestion, validation, transformation, storage, and access, ensuring performance and data integrity
• Apply statistical and machine learning methods to large-scale biomedical datasets
• Leverage modern computing approaches, including parallel processing, GPU acceleration, and distributed systems, where appropriate
• Implement best practices in software engineering, including version control, testing, and documentation
• Optimize systems for performance, scalability, and reliability
• Communicate technical designs and trade-offs clearly to both technical and scientific collaborators
• Contribute to shared libraries, internal platforms, and long-term architectural direction

• Experience applying statistical modeling and/or machine learning methods to genomics, imaging, or clinical data
• Experience in biotech, life sciences, or healthcare data environments
• Proficiency in one or more programming languages commonly used in data-intensive systems (e.g., Python, R)
• Familiarity with data storage and management systems, including SQL/NoSQL databases, object storage, data lakes, and data warehouses
• Familiarity with common genomics and imaging data formats (e.g., BAM, VCF, DICOM, OME-TIFF)
• Exposure to scalable computing environments, such as cloud platforms, HPC systems, or distributed computing frameworks

• Strong statistical thinking and analytical reasoning
• Experience building reliable, scalable computational systems for scientific applications
• Solid software engineering practices with emphasis on code quality, testing, and maintainability
• Ability to operate effectively in multidisciplinary research environments
• Interest in solving complex, real-world scientific problems
• Commitment to reproducibility and robust research computing practices