AI Data Scientist - Digital Pathology & Oncology (Hybrid)

University of Texas MD Anderson Cancer Center; Houston (TX); Hybrid; USD 107,000 - 160,000; Full time

Benefits offered by this job

Referral bonus

Relocation assistance

Job summary

The University of Texas MD Anderson Cancer Center seeks a Data Scientist to develop AI for pathology, multimodal data, and oncology. You will build pipelines, evaluate models, and contribute to translation toward clinical impact.

You will join an interdisciplinary team, publish work in top venues, mentor trainees, and help design experiments generating high-quality data for machine learning in cancer research.

Qualifications

• Bachelor's degree in a related STEM field is required.
• Master's or PhD degrees are preferred for advanced research.
• 3 years of scientific software or industry development/analysis experience required, or 1 year with Master's.

Responsibilities

• Develop AI technologies for pathology and multimodal data.
• Publish work in high-impact journals and ML conferences.
• Collaborate with inter-disciplinary teams to translate findings into clinical context.
• Design experiments to generate high-quality data for ML.
• Mentor junior trainees and support their projects.

Skills

Digital pathology experience

AI/ML development

Interdisciplinary collaboration

Education

Bachelor's Degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field

Master's Degree in Science or related field

PhD in Science or related field

Tools

Python

TensorFlow/PyTorch

SQL

Cloud platforms (AWS/Azure)

Job description

The University of Texas MD Anderson Cancer Center seeks a Data Scientist to develop AI for pathology, multimodal data, and oncology. You will build pipelines, evaluate models, and contribute to translation toward clinical impact.

You will join an interdisciplinary team, publish work in top venues, mentor trainees, and help design experiments generating high-quality data for machine learning in cancer research.