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Data Scientist / Imaging Physics - Research
The University of Texas MD Anderson Cancer Center invites applications for a Data Scientist (Computational Scientist) to join the Surgical Data Science Program within the Institute for Data Science in Oncology (IDSO). IDSO is composed of five major focus areas dedicated to advancing data science and emerging medical technologies to improve clinical care and operational excellence.
Within the IDSO focus area on Quality, Safety, and Access, the Surgical Data Science Program brings together experts in data science, systems engineering, and technology development. This multidisciplinary team collaborates closely with surgeons, physicists, and engineers to deliver impactful research and translation in:
• Multidimensional data analysis (including medical imaging)
• Predictive modeling
• Clinical outcomes research
• Applied machine learning and AI in surgical and perioperative care
Shift / hours: Based on business needs and 100% onsite
The ideal applicant will have demonstrated experience with/in computing / programming (Python, Matlab, C++, CUDA, Julia, R and/or SQL), machine learning / deep learning methods including neural network design, computer vision / image analysis, and statistical analysis
Key Responsibilities
The Data Scientist will:
• Develop and apply advanced machine learning, deep learning, computer vision, and statistical modeling techniques.
• Conduct multidimensional data analyses, including medical image data, clinical data, and outcomes.
• Contribute to predictive modeling, decision-support tools, and data-driven clinical insights.
• Collaborate with a multidisciplinary team across surgery, physics, engineering, and data science.
• Support research translation into clinical workflows and operational improvement initiatives.
Required Technical Expertise
Candidates should demonstrate experience with:
Programming & Computing
• Python, MATLAB, C++, CUDA, Julia, R, and/or SQL
Machine Learning / Deep Learning
• Neural network design and implementation
• Computer vision and image analysis
• Applied statistics and data modeling
Preferred Experience with Scientific Computing Libraries
• SciPy, pandas, statsmodels
• OpenCV, XGBoost
• PyTorch and/or TensorFlow
• SQL or other database querying languages
Preferred Experience with Modern ML/Statistical Techniques
• Regression, clustering, segmentation
• Time-series analysis
• Recommender systems
• Predictive analytics
• Bayesian modeling
• Data fusion
• Supervised and unsupervised learning
• Decision trees, A/B testing
• Natural language processing (NLP)
EDUCATION
- Required: Bachelor's Degree Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field.
- Preferred: Master's Degree Science, Engineering or related field.
- Preferred: PhD Science, Engineering or related field.
WORK EXPERIENCE
- Required: 3 years Scientific software or industry development/analysis experience. or
- Required: 1 year Required experience with Master's degree. or
- Required: With PhD, no experience required.
The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state, or local laws unless such distinction is required by law.http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html
