Cheminformatics Scientist

ResponsibilitiesPrepare and structure reaction datasets (e.g., ELNs, Reaxys, USPTO) for machine learning pipelinesDevelop and improve models for forward reaction prediction and single/multi-step retrosynthesisDesign and benchmark algorithms for reaction condition recommendation, including yield and selectivity modelingImplement both template-based and template-free approaches using reaction SMARTS, graph methods, or transformer architecturesLead or support the development of closed-loop optimization frameworksCollaborate cross-functionally to deploy models into production cheminformatics platformsCommunicate model results, insights, and design considerations to both technical and non-technical stakeholdersStay up-to-date with relevant literature, tools, and methodologiesRequired QualificationsPhD in Chemistry, Cheminformatics, Computational Chemistry, Machine Learning, or a related fieldSolid understanding of organic chemistry principles, synthesis strategies, and retrosynthetic logicExperience with tools such as ASKCOS, IBM RXN, AiZynthFinder, or similar platformsProficiency in cheminformatics libraries like RDKit, Indigo, Open Babel, or CDKSkilled in machine learning using Python (e.g., scikit-learn, PyTorch, TensorFlow)Hands-on experience with reaction classification, yield prediction, or optimization modelsAbility to manage and process chemical data formats (e.g., JSON, XML)Familiarity with SQL and NoSQL databases (e.g., MySQL, MongoDB)Understanding of Bayesian optimization, active learning, or closed-loop experimentationBonus QualificationsExperience integrating ML workflows into robotic chemistry platforms or autonomous labsContributions to open-source projects (e.g., RDKit, ASKCOS)Experience with advanced ML models like transformers, GNNs, or seq2seq architecturesFamiliarity with cloud services (AWS/GCP) and containerization (Docker, Kubernetes)Experience in deploying models into production or chemistry SaaS environments