Data Scientist Molecular Modeling

We seek a talented, energetic, and collaborative Molecular Modeling Scientist to conduct both routine and novel analyses as part of our flagship platform A3D3a:
Adaptive, AI-augmented, Drug Discovery and Development. With experience in structural prediction, comparative modeling, and emerging generative models, the Molecular Modeling Scientist will contribute to the discovery of novel cancer therapeutics as part of A3D3a.

Led by Prof. Bissan Al-Lazikani, Director of Therapeutics Data Science, the intelligent and ever-learning A3D3a platform is part of MD Anderson’s Therapeutics Data Science initiative and part of our ambitious Institute for Data Science in Oncology. A3D3a will accelerate the discovery and impact of novel therapies for cancer by enabling novel opportunities for optimized therapies for patients with a focus on rare and hard-to-treat cancers through the development of novel machine learning and AI technologies.

Central to this vision, the Molecular Modeling Scientist will leverage state-of-the-art macromolecular modeling techniques and, collaboratively with senior scientists, develop innovative data science approaches to answer structural questions about and nominate therapeutic targets. Additionally, physics-based modeling may also complement structural informatics and generative AI efforts. The preferred candidate should have at least an MSc in Chemistry, Biochemistry, Computer Science, or a related field.

A strong background in computational modeling and a willingness to learn new approaches is critical to this role.

The ideal candidate will be an experienced computational scientist with strong Python-based scientific computing skills and expertise in cheminformatics, generative AI, and molecular modeling—capable of building predictive ML models, generating and analyzing macromolecular structures, leveraging tools such as PyTorch, Modeller, Rosetta, and MD packages, and contributing effectively to multidisciplinary research through rigorous documentation, scientific communication, and efficient project management.

• Generate comparative macromolecular models of potential therapeutic targets
• Build and apply machine/deep learning models to predict structural properties
• Analyze ensembles of structures through techniques such as clustering
• Document work thoroughly
• Produce output for scientific publications and contribute to said publications
• Present results at multidisciplinary project meetings as well as external meetings
• Attend collaborator meetings and team working group meetings. Prioritize and manage multiple projects in a timely and resource‑effective manner
• Stay up to date with relevant literature, gather information systematically, and confer with the supervisor regarding new procedures
• Expertise in scientific computing using Python in a Unix environment
• Proficient with generative AI for structure prediction; familiar with Py Torch
• Proficiency with Modeller, Rosetta, or other classical comparative modeling tools
• Experience with OpenMM, AMBER, or comparable MD tools
• Experience with molecular visualization tools (e.g., PyMol, VMD)
• Familiar with basic statistical testing

Other duties as assigned.

Required:

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

Preferred:
Master's or PhD Degree in Science, Engineering or related field.

Required:

Three years scientific software or industry development/analysis experience. With Master's degree, one year. With PhD, no experience required.

Preferred:
Proficient with PyTorch. Proficient with comparative modeling tools.

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.…