Job Description

• Chemistry Data Curation & Structure Management
• Curate, validate, and maintain chemical structure data used across research platforms and scientific workflows.
• Perform structure standardization and normalization, including salt or solvent handling, valence checks, charge normalization, tautomer treatment, stereochemistry review, and duplicate identification.
• Support compound registration, structure correction, structure 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 that 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.

• 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.
• 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.

• 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.

• Python engineering and scientific software development
• Chemistry curation and molecular data quality
• Cheminformatics and structure-processing workflows
• Analytical problem-solving across chemistry data and code
• Collaboration with scientific and technology stakeholders
• Delivery of reliable, documented, and maintainable solutions

• Higher quality, consistency, and traceability of curated chemistry data.
• Reduced manual effort through dependable Python automation and self-service tools.
• Scalable, maintainable chemistry workflows that meet scientist and business needs.
• Effective connection between chemistry subject-matter experts and engineering teams.
• Reusable data and software foundations that support analytics and AI-enabled molecular research.

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