Materials Solutions Platform Development & Strategic Impact- Collaborate with platform architects, Data Scientists and product managers to define data-driven features for Materials (e.g. polymer selection, formulation prediction, and performance benchmarking) across multiple application segments.
Analyze and interpret materials or polymer simulation data (MD, DFT, coarse-grained) using LAMMPS, GROMACS, Materials Studio, VASP and experimental data from multiple sources to identify meaningful PSPP correlations.
Develop and structure a PSPP schema that integrates simulation descriptors, experimental properties, and processing metadata using Python, pandas, NumPy, Matplotlib.
Define entity relationships and standard vocabularies for PSPP integration with ontology and schema development teams.
Collaborate with data scientists and informatics engineers to embed PSPP relationships into knowledge graphs and search platforms using Neo4j, GraphQL, RDF.
Support predictive model development and intelligent search tools that leverage structured PSPP data using scikit-learn, TensorFlow, PyTorch.
Document simulation workflows, model assumptions, and data lineage for scientific traceability and reproducibility using Jupyter Notebooks, GitHub, Docker.
Ideal Candidate will have
Ph.D. in Polymer Science, Materials Science, Computational Chemistry, Chemical Formulation, Materials Engineering or related field with 37 years of relevant experience in industry like Personal Care & Cosmetics, Adhesives, Packaging etc.
Strong foundation in materials (e.g. polymer) simulation and data analysis techniques and ability to relate them to experimental trends and data.
Skilled at identifying structurepropertyprocessingperformance patterns from diverse data sources.
Familiarity with materials informatics framework, search platforms, or AI-driven tools.
Strong communication skills for cross-functional collaboration with informatics, data science, and product teams.
Bonus: Experience working with schema development (XML, JSON, OWL) or materials ontology platforms like Protg.
Job Requirements
Ph.D. in Computational Polymer Science/ Materials Science/ Chemistry/ Chemical Formulation/ Materials Engineering/ Chemical Engineering with 3-5+ years of experience in materials (e.g. polymer) simulation and data analysis, including molecular dynamics or multi-scale modeling.
Proficiency with LAMMPS, GROMACS, VASP, Materials Studio, and data processing tools (Python, pandas).
Demonstrated ability to work with both simulation and experimental data to draw scientific relationships.
Understanding of informatics tools, ontologies (OWL, RDF), and schema development (XML, JSON) (preferred but not mandatory).
Proficiency in Python or equivalent scripting for data processing and automation.
Familiarity with materials informatics framework, search platforms, or AI-driven tools
