Data Scientist Molecular Modeling

MD Anderson Cancer Center

Houston (TX)

On-site

USD 106,500 - 159,500

Full time

14 days+

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Benefits offered by this job

Medical benefits

Dental benefits

Paid time off

Retirement benefits

Tuition benefits

Educational opportunities

Team recognition

Job summary

A leading cancer research institute in Houston seeks a Molecular Modeling Scientist to drive the development of AI-enhanced drug discovery. This role involves conducting structural modeling and analysis for novel cancer therapeutics. Candidates should have strong Python skills, expertise in generative AI, and a solid foundation in cheminformatics. Excellent benefits, hybrid work opportunities, and a focus on innovative drug research are part of the offering.

Qualifications

At least an MSc in Chemistry, Biochemistry, Computer Science, or related field.
Strong background in computational modeling.
Willingness to learn new approaches.

Responsibilities

Generate comparative macromolecular models of potential therapeutic targets.
Build and apply machine/deep learning models to predict structural properties.
Document work thoroughly and present results.

Skills

Python programming

Cheminformatics

Generative AI

Experimental techniques

Education

Bachelor's Degree in a relevant field

Master's or PhD in a related field

Tools

PyTorch

Modeller

Rosetta

AMBER

OpenMM

PyMol

VMD

Job description

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 compliment 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.

Job Responsibilities

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 PyTorch.
Proficiency with Modeller, Rosetta, or other classical comparative modeling tools
Experience with OpenMM, AMBER, or comparable MD tools
Experience with molecular visualization tools (i.e., PyMol, VMD)
Familiar with basic statistical testing

Other duties as assigned.

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 or PhD Degree Science, Engineering or related field.

Experience

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. 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

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