Bioinformatics Scientists

Conduct research using bioinformatics theory and methods in areas such as pharmaceuticals, medical technology, biotechnology, computational biology, proteomics, computer information science, biology and medical informatics. May design databases and develop algorithms for processing and analyzing genomic information, or other biological information.

The automation risk for Bioinformatics Scientists is calculated at 51.4%, which is closely aligned with the base risk of 52.5%. This moderate risk reflects the combination of both technical and creative tasks involved in this occupation. While the field relies heavily on computational methods and data processing, it also demands specialized domain knowledge and problem-solving abilities that are not easily replicated by machines. As automation technologies and AI continue to advance, tasks that follow clear, repeatable patterns are more susceptible to becoming automated. However, the need for human insight, flexibility, and originality means that total automation remains unlikely in the near future. Among the most automatable tasks for Bioinformatics Scientists are developing new software applications or customizing existing ones to fit specific scientific needs, communicating research results through channels such as conference presentations and publications, and creating novel computational approaches or analytical tools in line with research objectives. These tasks often involve some repetitive elements, standardized workflows, or the application of existing algorithms, making them prime candidates for automation. For example, software development can increasingly be supported by AI-assisted code generation, and automated report templates can streamline the communication of findings. Even generating new analytical tools, while complex, may sometimes follow recognizable computational patterns that machines can learn and optimize. Conversely, certain core responsibilities remain resistant to automation due to the need for nuanced expertise and human judgment. Tasks such as preparing summary statistics on human genomes require the ability to interpret complex biological data and context, which is not easily codified. Testing new bioinformatics tools involves critical evaluation and troubleshooting skills, often dealing with ambiguous results and unforeseen edge cases. Collaboration with software developers during the creation or modification of commercial bioinformatics software requires adaptability, negotiation, and domain-specific knowledge that goes beyond pure programming. Moreover, bottleneck skills such as originality—which scores 3.8% and 4.3% in its assessed levels—highlight the creative and interpretative components important for successful performance in this field, further impeding complete automation.