Forward-Deployed Cheminformatician
New
SwitzerlandFull-TimeMiddle
SalaryCompetitive compensation package.
Apply NowOpens the employer's application page
Job Details
- Experience
- At least 3 years of experience
- Required Skills
- PythonData engineering
Requirements
- Bachelor's, Master's, PhD, or equivalent qualification in Cheminformatics, Computational Chemistry, Pharmaceutical Sciences, or a related scientific discipline.
- At least 3 years of experience preparing and curating biological assay data within drug discovery, pharmaceutical research, or related scientific environments.
- Strong proficiency in Python and RDKit, including expertise in molecular representation, SMILES normalization, stereochemistry, scaffold extraction, tautomer handling, and related cheminformatics techniques.
- Hands-on experience working with quantitative binding assay data, including KD, Ki, IC50, pIC50, high-throughput screening data, and associated metadata interpretation.
- Proven ability to develop maintainable, well-tested code, implement validation frameworks, and work with modern software engineering practices and version control systems.
- Strong communication and stakeholder engagement skills, with the ability to collaborate effectively with scientific and technical audiences.
- Demonstrated ability to work independently, manage ambiguity, and transform complex data challenges into structured, repeatable processes.
Responsibilities
- Define, develop, and maintain standardized binding-data preparation protocols, including data schemas, assay metadata structures, value normalization, duplicate handling, and quality control processes.
- Build and improve modular tooling, validation frameworks, and reusable pipelines that support diverse pharmaceutical data sources and ensure consistency across projects.
- Collaborate directly with pharmaceutical researchers, medicinal chemists, and biologists to interpret complex assay data, validate data quality, and facilitate successful data onboarding.
- Maintain and optimize small-molecule processing workflows, including molecular standardization, stereochemistry preservation, tautomer handling, ionization management, and filtering methodologies.
- Curate and harmonize large-scale public binding-data resources to support model development, benchmarking, and scientific research initiatives.
- Partner closely with engineering and machine learning teams to ensure data pipelines remain scalable, maintainable, and aligned with evolving product and research requirements.
- Drive documentation, process standardization, and knowledge sharing to transform manual data preparation efforts into repeatable and reliable workflows.
View Full Description & ApplyYou'll be redirected to the employer's site
