Junior AI Scientist for Materials

We are Covestro. We are curious. We are courageous. We are colorful. We refine chemical material solutions with game-changing products. Let us empower you to push boundaries. Join us and our 18.000 colleagues now and together we will make the world a brighter place.

MAJOR TASKS & RESPONSIBILITIES

• Design build and deploy LLM/RAG applications addressing specific materials R&D workflows: polymer literature analysis and synthesis route discovery formulation design assistant experimental data extraction and structuring technical documentation generation and property-structure relationship exploration

• Work embedded with polymer chemists and formulation scientists to understand polymerization processes coating/adhesive formulations and material characterization workflows; identify pain points and translate them into structured AI use cases with clear problem statements and success metrics

• Implement production-grade AI solutions using modern frameworks (Streamlit FastAPI Gradio) with embeddings vector databases and prompt engineering techniques; integrate chemistry-specific knowledge (SMILES polymer structures material properties formulation parameters) into AI models

• Build and maintain data pipelines for materials data: chemical structure parsing property data cleaning experimental result extraction from reports feature engineering for polymer descriptors and visualization of structure-property relationships

• Define user journeys for R&D scientists working on polymer synthesis formulation optimization and material testing; prioritize features based on R&D impact and feasibility through small iterative milestones and continuous user feedback

• Apply data science best practices to materials datasets: exploratory data analysis of formulation spaces statistical analysis of experimental results visualization of polymer property trends and basic predictive modeling for material performance

• Ensure code quality through clean architecture modularization proper git workflows comprehensive documentation and engineering mindset focused on maintainability and scalability

• Stay current with AI for science developments (materials informatics molecular LLMs generative chemistry models); contribute to APAC Digital R&D capability building through knowledge sharing tutorials and best practice documentation

• Collaborate with computational chemistry and materials modeling teams to integrate AI-driven approaches with physics-based simulations for polymer design and formulation optimization

• Engage with external AI/materials research communities universities and technology providers to track emerging methodologies; support cross-BE digital R&D projects and contribute to IP strategy for AI-generated materials insights

REQUIRED QUALIFICATIONS

Education & Experience

• Masters degree in Computer Science Data Science Chemistry Materials Science Chemical Engineering Polymer Science or related fields

• 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 to deployment

Technical Skills

Must Have:

• Strong Python programming with clean structured coding practices

• Web application frameworks: Streamlit FastAPI Flask or similar

• LLM/RAG fundamentals: embeddings and vector databases (ChromaDB Pinecone FAISS) prompt engineering and chain-of-thought reasoning API integration with LLM providers (OpenAI Azure OpenAI Claude etc.)

• Data processing & analysis: pandas numpy data cleaning EDA visualization (matplotlib plotly seaborn)

• Version control: Proficient with git workflows (branching PR code review)

Good to Have:

• Experience working with chemistry/materials data (chemical structures molecular properties polymer characterization data formulation compositions)

• Knowledge of cheminformatics libraries (RDKit for molecular handling mordred for descriptor calculation)

• Familiarity with machine learning for molecules/materials (QSAR/QSPR property prediction molecular fingerprints)

• Understanding of polymer chemistry fundamentals (monomer-polymer relationships polymerization mechanisms structure-property principles)

• Cloud deployment experience (Azure AWS containerization with Docker)

• Knowledge of laboratory digitalization electronic lab notebooks (ELN) and FAIR data principles

Soft Skills & Mindset

• Product thinking: Ability to define problems clearly break complex R&D challenges into actionable steps think from chemists user perspective prioritize features based on impact

• R&D domain curiosity: Genuine interest in chemistry and materials science; willingness to learn polymer structures formulation principles and characterization techniques by observing lab workflows and talking to chemists

• Engineering mindset: Care about code maintainability documentation quality scalability and production-grade development practices

• Collaboration: Comfortable working across disciplines with polymer chemists formulation scientists process engineers and business stakeholders

• Agile learner: Quick to adopt new AI tools chemistry software and methodologies in fast-evolving AI-for-science field

• Communication: Able to explain technical AI concepts to chemists and chemistry concepts to data scientists; document work clearly in both technical and business contexts

Language

• English: Fluent (working language for code documentation and collaboration with global teams)

• Mandarin Chinese: Professional proficiency (for daily interaction with APAC R&D teams)

WORK RELATIONS

• APAC Digital R&D team computational chemistry and materials modeling teams

• Polymer chemists formulation scientists and R&D engineers across BEs in PRDC Shanghai and ICJ Amagasaki

• Group Innovation & Sustainability digital R&D community

• IT/Cybersecurity Data Governance HSEQ teams

• Universities and research institutes working on AI for materials

• AI/ML technology providers and open-source communities

• Industry consortia on materials informatics and scientific AI

VALUE ADDED

• Accelerate polymer R&D cycles through AI-powered synthesis route discovery formulation design assistance and automated experimental data analysis

• Boost chemist productivity by automating repetitive technical tasks (literature review data extraction report generation) and enabling self-service AI tools

• Democratize AI in materials R&D by building user-friendly applications that make advanced AI accessible to bench chemists and formulation scientists

• Establish competitive advantage in polymer innovation through early adoption of LLM and generative AI technologies for materials discovery

• Contribute to APAC Digital R&D portfolio and strengthen innovation hub capabilities in data science/ML for chemistry and materials

LOCATION

Primary: PRDC Shanghai China
Occasional travel: ICJ Amagasaki (Japan) other Covestro sites in APAC

YOUR APPLICATION
Are you as curious courageous and colorful as we are If so we can offer you an exciting career opportunity. We look forward to receiving your application including a cover letter resume/CV and relevant certificates. Become part of our Team - apply now!

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