Senior Scientist – Computational siRNA Lead Development

39d

About The Position

We are seeking a highly experienced and hands-on scientist to lead computational siRNA design and selectivity assessment efforts. This role will work closely with an experienced Bioinformatician to build and implement in-house workflows that integrate oligonucleotide chemistry, transcriptomic profiling, and predictive modelling to enable robust lead nomination. The ideal candidate combines deep RNAi mechanistic expertise with strong computational skills, a track record of advancing oligonucleotide therapeutics in a drug discovery setting and thrives when collaborating with an R&D team.

Requirements

Ph.D. in Computational Biology, Bioinformatics, Molecular Biology, or related discipline.
Extensive industry experience in drug discovery with significant depth in RNA-targeted therapeutics (siRNA, ASO, splice-switching oligonucleotides).
Demonstrated hands-on expertise in transcriptome-wide off-target analysis.
Deep understanding of RNAi biology and oligonucleotide chemistry.
Strong programming skills in Python and/or R.
Experience building and implementing computational workflows in a research environment.

Responsibilities

Work closely with a Senior Bioinformatician to review and interpret computationally generated siRNA designs.
Apply core RNAi principles—including thermodynamic asymmetry, strand bias, RISC loading, seed-region effects, and target accessibility—to evaluate candidate sequences.
Translate computational design outputs into actionable experimental strategies.
Guide sequence selection decisions based on potency, selectivity, and developability considerations.
Apply deep knowledge of oligonucleotide chemistry, including modified nucleotides (2’-O-Me, 2’-F, LNA), backbone modifications (PS), and conjugation strategies (e.g., GalNAc, lipid-based delivery).
Partner with chemistry teams on structure–activity relationship (SAR) studies, including novel chemistries.
Integrate chemical architecture and screening data into lead optimisation decisions.
Design and analyse bulk and single-cell RNA-seq experiments to quantify on-target knockdown and off-target effects.
Execute transcriptome-wide selectivity profiling approaches (e.g., concentration-response digital gene expression).
Distinguish hybridization-dependent and hybridization-independent effects.
Develop analytical frameworks to assess seed-mediated, partial complementarity, and chemistry-driven off-target mechanisms.
Conduct cross-species selectivity analyses to support translational development.
Lead the organization and maintenance of internal siRNA sequence datasets and associated screening results.
Implement and maintain sequence databases and visualisation tools that enable exploration of sequence features, transcriptomic results, and screening data.
Ensure data traceability and accessibility across discovery programs.
Establish best practices in computational reproducibility and workflow automation.