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Computational Scientist Rdkit information

What is a computational scientist RDKit?

A Computational Scientist specializing in RDKit is a professional who uses computational methods and the RDKit cheminformatics toolkit to analyze and model chemical structures and reactions. They often work in fields like drug discovery, materials science, or chemical engineering, leveraging RDKit for tasks such as molecular fingerprinting, property prediction, virtual screening, and data visualization. Their expertise combines advanced programming skills, a strong understanding of chemistry, and the ability to develop or optimize algorithms for chemical data analysis.

What are the key skills and qualifications needed to thrive as a computational scientist RDKit, and why are they important?

To thrive as a Computational Scientist using RDKit, you need a strong background in cheminformatics, computational chemistry, and programming, typically with an advanced degree in chemistry, bioinformatics, or a related field. Proficiency in Python, familiarity with RDKit libraries, and experience using molecular modeling and data analysis tools are essential. Critical thinking, problem-solving, and effective collaboration are important soft skills for translating scientific questions into computational solutions. These competencies enable accurate molecular data analysis, innovation in research, and successful teamwork in interdisciplinary environments.

What is the difference between Computational Scientist Rdkit vs Computational Chemist?

AspectComputational Scientist RdkitComputational Chemist
Required CredentialsDegree in Chemistry, Bioinformatics, or related; programming skills in Python; familiarity with RDKitDegree in Chemistry, Chemical Engineering, or related; strong background in molecular modeling and programming
Work EnvironmentResearch labs, biotech companies, pharmaceutical firms; focus on software development and data analysisAcademic or industrial labs; focus on experimental design, molecular simulations, and data interpretation
Industry UsageUsed in cheminformatics, drug discovery, and molecular data analysisApplied in pharmaceuticals, materials science, and chemical research

Computational Scientist Rdkit specializes in developing and applying cheminformatics tools using RDKit, often combining programming and data analysis. Computational Chemist focuses on molecular modeling, simulations, and chemical research. While both roles require chemistry knowledge and programming skills, the Computational Scientist Rdkit role emphasizes software development and data processing, whereas the Computational Chemist emphasizes experimental and theoretical chemistry applications.

How does a computational scientist RDKit typically collaborate with chemists and software engineers on research projects?

Computational Scientists specializing in RDKit often work closely with chemists to translate scientific questions into computational workflows, such as molecule property prediction or virtual screening. They also collaborate with software engineers to integrate RDKit functionalities into larger platforms or to optimize code for performance and scalability. Effective communication and project management are essential, as these interdisciplinary teams rely on regular meetings, shared documentation, and iterative feedback to ensure research goals are met efficiently. This collaborative environment not only fosters scientific innovation but also provides opportunities to learn from experts in related fields.
What are popular job titles related to Computational Scientist Rdkit jobs in Texas? For Computational Scientist Rdkit jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Computational Scientist Rdkit jobs in Texas look for? The top searched job categories for Computational Scientist Rdkit jobs in Texas are:
Computational Scientist Rdkit jobs near you
Infographic showing various Computational Scientist Rdkit job openings in Texas as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 88% Full Time, 7% Part Time, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Postdoctoral Associate

Baylor College of Medicine

Houston, TX • On-site

Full-time

Re-posted 14 days ago

Baylor College of Medicine rating

8.0

Company rating: 8.0 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

187th of 618 rated colleges and universities

Job description

Summary

Postdoctoral positions in cheminformatics are available in the Zhi Tan laboratory at Baylor College of Medicine (Houston, TX), an interdisciplinary group using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Postdoctoral Associate with a proven track record in developing open-source machine learning, deep learning, or cheminformatics tools.

Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.

Job Duties
  • Plans, directs and conducts research experiments.
  • Develops research techniques and perform applications required for specific research projects.
  • Conducts literature searches and summarize information in an appropriate format for a particular study.
  • Documents results of experiments and reports to principal investigators. 
  • Performs other job-related duties as assigned.
Minimum Qualifications
  • MD or Ph.D. in Basic Science, Health Science, or a related field.
  • No experience required.
Preferred Qualifications
  • Doctoral Degree in Computational Chemistry, bioinformatics, computational biology, or related discipline. Experience may not be substituted in lieu of degree.
  • Experience in cheminformatics software development.
  • Experience in developing machine learning, deep learning tools, especially with application in drug discovery.
  • Experience with high performance computing environment (HPC) / cluster job submission.
  • Knowledge of statistical methods, data science algorithms, scientific and numerical computation.
  • Familiarity with common Python tools including Pandas, Numpy, Scipy, Django, RDKit.
  • Proficiency in coding and debugging in Pytho.
  • Strong knowledge and experience with relational databases (e.g. Oracle, SQL, MySQL).
  • Comfortable working in a Linux environment.
  • Experience with data processing pipelines and data analysis.
  • Excellent communication skills with a diverse team of biological and chemical scientists.
  • Experience with development of open-source computational tools (machine learning, deep learning, cheminformatics), especially with application in drug discovery.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.

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