Computational Biology Specialist (Drug Discovery & AI Training)

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 Computational Biology Specialist to support a scientific project focused on improving how advanced AI systems understand biological data and medicinal chemistry problems.

You will analyse complex datasets, review scientific content, develop realistic drug-discovery scenarios, and provide expert feedback on computational biology outputs. The role combines bioinformatics, medicinal chemistry, biological data analysis, and scientific evaluation. 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 and annotate complex biological and chemical datasets.
• Review scientific content for accuracy, relevance, and clarity.
• Evaluate AI-generated responses related to computational biology and medicinal chemistry.
• Develop realistic problem sets, case studies, and drug-discovery scenarios.
• Apply computational methods to biological and chemical research questions.
• Identify data-quality issues, scientific gaps, and unsupported conclusions.
• Provide structured feedback to improve AI model performance.
• Contribute to data-curation methods and project quality standards.

• Advanced degree in Computational Biology, Bioinformatics, Medicinal Chemistry, or a related field.
• Experience applying computational methods to biological or chemical problems.
• Strong knowledge of medicinal chemistry, drug discovery, or molecular design.
• Experience analysing biological datasets and scientific literature.
• Proficiency with relevant bioinformatics, cheminformatics, or data-analysis tools.