Sobre este puesto de Bioinformatics Scientist (Drug Discovery & AI Training) en Gramian Consulting Group
About Us
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
Role Overview
We are looking for an experienced Bioinformatics Scientist to support a scientific project focused on improving how advanced AI systems understand biological, chemical, and drug-discovery data.
You will analyse and validate complex datasets, review AI-generated scientific content, and contribute expert insights across bioinformatics, medicinal chemistry, cheminformatics, and molecular analysis. Previous AI experience is not required.
CONTRACT: Freelance contractor, paid per completed task
COMMITMENT: Flexible, based on available tasks and project demand
LOCATIONS: Fully remote - GLOBAL
PROCESS: Application review, technical assessment, and onboarding
HOURLY RATE: $90-$120/h
Responsibilities
- Analyse biological, chemical, and clinical datasets relevant to drug discovery.
- Curate, annotate, and validate scientific data.
- Evaluate AI-generated outputs for accuracy, relevance, and reliability.
- Interpret findings from omics, cheminformatics, and molecular datasets.
- Develop scientific content and realistic medicinal chemistry scenarios.
- Provide input on experimental design and data interpretation.
- Identify data-quality issues, inconsistencies, and unsupported conclusions.
- Deliver structured feedback and recommendations to improve AI outputs.
Requirements
- Advanced degree in Bioinformatics, Computational Biology, Medicinal Chemistry, or a related field.
- Experience analysing large-scale biological, omics, or cheminformatics datasets.
- Strong knowledge of medicinal chemistry, structure–activity relationships, or drug-design principles.
- Proficiency with bioinformatics or cheminformatics tools and programming languages such as Python, R, RDKit, or KNIME.
- Experience interpreting scientific literature and communicating technical findings clearly.