STEM Expert – Biology & Bioinformatics

We are sharing a specialised remote consulting opportunity for experienced Biology, Bioinformatics, and Computational Biology Experts with advanced expertise in biological data analysis, scientific programming, genomics, molecular biology, and quantitative research to contribute their knowledge to AI training and benchmarking initiatives. Selected professionals will design, review, and evaluate complex biological research problems, computational workflows, and scientific reasoning tasks. The work combines advanced domain expertise with genomic and transcriptomic analysis, proteomics, statistical modelling, bioinformatics software, and reproducible computational research to improve the scientific accuracy and analytical capabilities of advanced AI systems. Key Responsibilities - Biological Research & Scientific Problem Development - Develop technically rigorous biology and computational biology problems reflecting realistic research scenarios - Analyse complex scientific questions involving molecular biology, genetics, cellular processes, and biological systems - Apply specialised biological knowledge to evaluate research assumptions, experimental methods, and analytical approaches - Design scientific reasoning tasks involving biological datasets, computational methodologies, and quantitative interpretation - Contribute expert-level insights to the development and evaluation of biology-focused AI benchmarking assignments - Genomics, Transcriptomics & Biological Data Analysis - Analyse genomic, transcriptomic, proteomic, and other biological datasets using appropriate scientific methods - Evaluate next-generation sequencing (NGS), RNA-seq, single-cell analysis, and variant-calling workflows - Review data processing pipelines involving sequence alignment, quality control, biological annotation, and downstream statistical analysis - Assess protein structures, molecular datasets, and relevant computational biology applications - Identify methodological inconsistencies, analytical errors, and opportunities to improve biological data interpretation - Bioinformatics Pipelines & Scientific Programming - Develop and evaluate computational workflows using Python, R, and relevant bioinformatics software - Assess scientific software configurations, pipeline parameters, and computational methods for accuracy and reproducibility - Apply appropriate tools such as BLAST, samtools, GATK, Bioconductor, Biopython, or equivalent applications - Review statistical techniques, data transformations, and computational procedures used in biological research - Support the development of reproducible analyses using appropriate scripting, workflow management, and scientific computing practices - AI Training & Computational Biology Evaluation - Design and critique biology-related questions, bioinformatics workflows, and scientific reasoning chains for AI benchmarking - Review AI-generated biological analyses, computational solutions, and scientific explanations for correctness and completeness - Identify errors involving biological assumptions, data interpretation, statistical reasoning, and computational procedures - Develop technically accurate reference solutions supported by established biological principles and reproducible methodologies - Provide structured expert feedback to improve AI performance across genomics, molecular biology, bioinformatics, and computational research - Scientific Validation & Research Documentation - Evaluate biological research methodologies, computational outputs, and statistical conclusions for scientific consistency - Assess workflow reproducibility, software configurations, analytical assumptions, and data quality - Produce clear technical documentation describing research methods, computational procedures, and scientific findings - Communicate sophisticated biological and computational concepts effectively to scientific and interdisciplinary collaborators - Support consistent scientific quality standards, knowledge sharing, and methodological improvement across specialised research assignments Ideal Profile - PhD in Biology, Bioinformatics, Computational Biology, Genomics, or a closely related STEM discipline, or equivalent industry or research experience - Deep expertise in at least one relevant biological or computational research specialisation - Demonstrated hands-on experience analysing real biological datasets and developing scientifically rigorous interpretations - Strong understanding of biological research methodologies, molecular processes, and quantitative scientific analysis - Expertise in genomics, next-generation sequencing, transcriptomics, single-cell analysis, or related molecular data applications - Alternatively, advanced experience in proteomics, structural biology, molecular and cell biology, or systems biology - Experience in biological network analysis, drug discovery, computational pharmacology, or biostatistics is also relevant - Practical proficiency in Python and/or R for scientific programming, biological data analysis, and computational research - Familiarity with bioinformatics tools such as BLAST, samtools, GATK, Bioconductor, Biopython, or comparable specialised software - Strong statistical, quantitative, and analytical reasoning capabilities - Experience evaluating scientific software configurations, pipeline parameters, and computational results