Computational Chemist

About the work We're an early-stage specialty-chemistry company developing a novel functional fluid that must meet a tightly defined set of physical-property targets. Before we commit to laboratory synthesis, we need to screen and rank candidate molecular structures computationally — and determine, with confidence, which structures can and cannot meet the target property envelope. This is a focused, fast-moving validation project. The goal is a clear, well-documented go/no-go recommendation backed by reproducible calculations. The right person can make a real impact quickly, and there is a strong path to a permanent role as we scale. What you'll do Predict key physical properties of candidate molecules from structure: phase-transition / boiling behavior, thermal properties, electrical-insulation characteristics, and flammability-related parameters. Model phase equilibrium and mixture behavior, including vapor–liquid equilibrium and azeotrope prediction. Build and run a structured screening workflow that filters and ranks a candidate set against a defined property window. Apply group-contribution / QSPR methods for rapid first-pass estimates, and higher-level quantum-chemical calculations where precision is needed. Validate predictions against known reference compounds to establish confidence bounds. Document methods, assumptions, and results clearly enough for technical and non-technical stakeholders to act on. Required PhD or strong MSc in computational, physical, or theoretical chemistry (or closely related). Demonstrated experience predicting molecular physical properties from structure. Hands-on experience with phase-equilibrium / property-prediction tools — COSMO-RS / COSMOtherm strongly preferred — and/or established group-contribution and QSPR approaches. Proficiency in Python and a cheminformatics toolkit (e.g., RDKit) for building screening pipelines. Ability to work independently, define a defensible methodology, and communicate results crisply. Nice to have Quantum-chemistry / DFT experience (e.g., Gaussian, ORCA, or similar). Background in thermophysical-property estimation or process-simulation environments. Experience screening large candidate libraries and presenting ranked recommendations. To apply Please include: A short summary of a project where you predicted physical properties from molecular structure — the methods you used and how you validated them. Which property-prediction / phase-equilibrium tools you've worked with, and your depth in each. A brief example of a screening or property-prediction pipeline you've built (code samples or descriptions welcome; no confidential material).