Scientist/Senior Scientist, Computational Oligonucleotide Design

Key Responsibilities

• Drive RNA therapeutic design and computational optimisation, integrating RNA biology, transcript structure, RNA accessibility, and oligonucleotide chemistry, establishing and scaling computational pipelines to support rapid design and optimisation at scale.
• Develop automated data processing and visualization pipelines to support drug property analyses, including target specificity, cross-species targeting, and pharmacological properties.
• Develop and apply AI/ML frameworks to explore novel chemical space and to predict oligonucleotide drug properties, e.g. efficacy and toxicity, across diverse RNA targets.
• Analyze large biological datasets (e.g., transcriptomics, miRNA profiles, structural data) to inform oligonucleotide design and modification strategies.
• Develop models that integrate sequence, secondary/tertiary structure, and oligonucleotide chemistry to predict target engagement and guide binding assay design.
• Collaborate with oligonucleotide chemists, molecular biologists, and pharmacologists to translate computational predictions into experimentally validated oligonucleotide candidates, driving iterative design–test–learn cycles towards the identification of a drug candidate.
• Stay current with emerging computational tools, algorithms, and scientific literature relevant to the development of differentiated RNA therapeutics.
• Contribute to NATi’s intellectual property (IP) portfolio through computational tools and novel drug candidates.
• Prepare appropriate summary reports and presentations to update NATi’s leadership and stakeholders.

Qualifications & Experience

• D. in Computational Chemistry, Biophysics, Bioinformatics, Biomedical Engineering, or a related field.
• Minimum of 4 (Scientist) or 8 (Senior Scientist) years of hands-on industry experience in computational oligonucleotide design or RNA-targeting therapeutics.
• Strong computational skills, including hands-on experience developing scripts or workflows in R or Python, and applying machine learning frameworks to biological/chemical datasets, and familiarity with scientific computing libraries, git, and reproducible research practices.
• Proficiency in RNA structure prediction and interaction modelling tools (e.g., RNAstructure, ViennaRNA, IntaRNA, RNAhybrid).
• Hands-on experience with RNA-seq and sequence analysis tools and oligonucleotide design platforms.
• Strong understanding of RNA biology, including alternative splicing, polyadenylation, RNA editing, and RNA-binding proteins.
• Experience with nucleotide modification strategies (e.g., 2′-O-methyl, phosphorothioate, LNA) and their integration into computational design workflows to optimize oligonucleotide performance, spanning early design through late-stage candidate selection with strong understanding of risk assessment across discovery and development stages.
• Demonstrated experience combining physics-based modelling with AI/ML models to predict oligonucleotide efficacy, off-target effects, and/or RNA structure-function relationships using biological datasets is a plus.
• Proven ability to work independently and collaboratively in cross-functional, multidisciplinary teams, effectively communicating computational insights to experimental scientists.
• Demonstrated ability to troubleshoot complex scientific problems and adapt to shifting priorities in a fast-paced, quality-driven environment.