Python Cheminformatics Developer

Role Overview:This role focuses on the curation, validation, and management of chemical structure data, alongside the design and development of Python-based applications and automated workflows for scientific data processes. The successful candidate will apply cheminformatics expertise to support molecular analysis, data quality, and AI/ML initiatives within a research and development environment.Key Responsibilities:Curate, validate, and maintain chemical structure data across research platforms and scientific workflows.Perform structure standardization and normalization, including salt/solvent handling, valence checks, charge normalization, tautomer treatment, stereochemistry review, and duplicate identification.Support compound registration, structure correction, modification, and molecular metadata management.Investigate and resolve data-quality issues in collaboration with medicinal chemists, computational scientists, and informatics teams.Define and apply repeatable quality-control rules for chemistry datasets used in analytics and AI/ML initiatives.Design and develop Python-based applications, APIs, libraries, and automated workflows for chemistry and scientific data processes.Build scalable pipelines for structure ingestion, transformation, validation, standardization, and export.Integrate chemistry platforms, scientific databases, and enterprise or cloud services using APIs and data-integration patterns.Write modular, tested, maintainable code and contribute to code reviews, documentation, release practices, and production support.Create scientist-friendly utilities, notebooks, or lightweight interfaces to reduce manual curation effort and improve traceability.Use cheminformatics toolkits such as RDKit, ChemAxon, or equivalent technologies for molecular analysis and structure processing.Develop capabilities for substructure and similarity search, scaffold analysis, descriptor calculation, compound clustering, and molecular property computation.Apply chemistry knowledge to ensure software outputs are scientifically meaningful and fit for use.Contribute to data preparation and feature-generation workflows supporting predictive modeling, molecular design, and AI-enabled research.Partner with chemists, informaticians, data scientists, product owners, and software engineers to translate scientific needs into technical solutions.Participate in solution design, estimation, development, testing, validation, deployment, and operational support.Communicate technical decisions, chemistry-data risks, and delivery status clearly to scientific and technology stakeholders.Promote reusable engineering patterns, chemistry-data standards, and continuous improvement.Required Skills:Strong professional experience developing production-quality solutions in Python.Solid grounding in organic chemistry and chemical structure representation, including stereochemistry, salts, tautomers, charges, and structure-quality concepts.Hands-on experience with chemistry curation, compound registration, structure standardization, or molecular data management.Experience with Python scientific and data libraries such as pandas and Jupyter, plus SQL and REST APIs.Experience with at least one cheminformatics toolkit or chemistry platform, such as RDKit, ChemAxon, BIOVIA Pipeline Pilot, Dotmatics, Signals, or an equivalent platform.Ability to work across scientific and technical teams and explain complex concepts to varied audiences.Qualifications:Master's degree or PhD in Chemistry, Cheminformatics, Computational Chemistry, Pharmaceutical Sciences, Computer Science with substantial chemistry experience, or a related discipline. Equivalent relevant industry experience may be considered.Preferred Skills:Experience supporting pharmaceutical or life-sciences R&D, particularly small-molecule discovery or chemical synthesis workflows.Familiarity with compound registration systems, ELN platforms, chemical inventory tools, or scientific data platforms.Knowledge of cloud platforms, containers, CI/CD, automated testing, and secure software-development practices.Exposure to AI/ML applications involving molecular data, property prediction, molecule generation, or synthesis planning.Understanding of scientific data governance, auditability, validation, and regulated-environment expectations.