Materials Solutions Platform Development & Strategic Impact- Collaborate with Materials SME, platform architects and product managers to define data-driven features for Materials (e.g. polymer selection, formulation prediction, and performance benchmarking) across multiple application segments.
Data Extraction and Pipeline Development- Design and manage data extraction pipelines for materials-related documents (TDS, patents, publications) using Python, spaCy, ChemDataExtractor, OCR tools.
Build automated workflows for data cleaning, annotation, tagging, and mapping using pandas, NumPy, regex, Snakemake.
Semantic Modeling & Knowledge Graphs- Create structured databases and semantic knowledge graphs linking structure, properties, and applications using Neo4j, GraphDB, RDF, OWL.
Support ontology development in collaboration with domain scientists (e.g., polymer thesaurus, CAS Lexicon) using Protg, OWL, XML.
AI/ML Dataset Preparation- Deliver validated, AI-ready datasets for property prediction, formulation optimization, material recommendation, or performance analysis using scikit-learn, TensorFlow, PyTorch.
Collaborate with software, NLP, and product teams to integrate polymer data into customer-facing tools or internal dashboards.
Data Quality & Interoperability- Ensure data accuracy, consistency, and interoperability across all informatics workflows.
Document pipelines and models using Jupyter Notebooks and maintain reproducibility standards with version control (GitHub). Ideal Candidate will have
A data-oriented technologist with a passion for organizing and structuring scientific knowledge.
Solid foundation in data science or informatics, combined with a working understanding of structure-property-application relationships in materials/polymers.
Prior experience working with scientific or technical data from TDS, patents, or journals.
Skills in building pipelines, semantic models, and knowledge graphs for complex scientific data domains.
Mindset that values data Values clean data, reproducibility, and cross-disciplinary collaboration.
Job Requirements
B.Tech / M.Tech / Ph.D. in Data Science, Computer Science, Chemical Informatics, or Materials Informatics.
26 years of experience in informatics/data science roles with exposure to scientific/engineering domains.
Proficiency in Python, SQL, and data science libraries (pandas, spaCy, scikit-learn).
Experience with semantic modeling, graph databases (e.g., Neo4j, GraphDB), and knowledge representation standards (RDF, OWL).
Exposure to polymer/materials datasets and platforms like PoLyInfo, CAS SciFinder, MatWeb (preferred).
Familiarity with cheminformatics tools (RDKit, ChemAxon) and NLP pipelines for scientific text mining
