5 months ago
Pudong, ChinaEntry Level
Responsibilities
- Design, build, and deploy LLM/RAG applications for polymer literature analysis, synthesis route discovery, formulation design, experimental data extraction, documentation, and property-structure exploration.
- Work with polymer chemists and formulation scientists to identify R&D pain points, define AI use cases, and establish success metrics.
- Implement production-grade applications using web frameworks, embeddings, vector databases, prompt engineering, and chemistry-specific data.
- Build and maintain materials data pipelines for chemical structures, properties, experimental results, polymer descriptors, and structure-property visualizations.
- Define user journeys, prioritize features, and iterate based on R&D impact, feasibility, and user feedback.
- Perform exploratory data analysis, statistical analysis, visualization, and basic predictive modeling for materials performance.
- Maintain clean, modular, documented, scalable code and follow Git workflows, code review, and other engineering practices.
- Track AI-for-science developments, share knowledge across APAC Digital R&D, and collaborate with computational chemistry and materials modeling teams.
- Engage with universities, research communities, technology providers, and cross-business-unit digital R&D projects.
Requirements
- Master’s degree in Computer Science, Data Science, Chemistry, Materials Science, Chemical Engineering, Polymer Science, or a related field.
- 1–3 years of experience in data science, AI application development, or computational R&D.
- Demonstrated experience building at least one complete LLM/RAG application from concept through deployment.
- Strong Python programming and experience with Streamlit, FastAPI, Flask, or similar web application frameworks.
- Knowledge of embeddings, vector databases, prompt engineering, chain-of-thought reasoning, and LLM-provider API integration.
- Experience with pandas, NumPy, data cleaning, exploratory data analysis, and visualization using matplotlib, Plotly, or Seaborn.
- Proficiency with Git workflows, including branching, pull requests, and code review.
- Preferred experience with chemistry or materials data, RDKit, Mordred, QSAR/QSPR, molecular fingerprints, materials machine learning, polymer chemistry, Azure, AWS, Docker, laboratory digitalization, ELNs, or FAIR data principles.
- Fluent English and professional proficiency in Mandarin Chinese.
- Ability to collaborate across chemistry, materials science, engineering, data science, and business functions.
Benefits
- Primary work location is PRDC Shanghai, China, with occasional travel to ICJ Amagasaki in Japan and other Covestro sites in APAC.
- The role offers collaboration with global R&D teams, universities, research institutes, technology providers, and open-source communities.
- Applicants should submit a cover letter, resume/CV, and relevant certificates.
