Senior Scientist, siRNA Design and Off-Target Analysis

About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com . Follow @abbvie on LinkedIn, Facebook , Instagram , X and YouTube.

AbbVie's Biotherapeutic and Genetic Medicine (BGM) department is seeking a highly motivated Senior Scientist with deep expertise in computational biology, molecular design, and next-generation sequencing to support siRNA design and off-target assessment. This role will establish and optimize siRNA sequences with enhanced knockdown efficacy and reduced off-target liabilities, develop off-target assessment workflows, and integrate transcriptomic and genomic data. The ideal candidate will combine strong biological insight with quantitative skills to advance siRNA design, identify potential off-target risks, and contribute to the development of safer, more selective RNA-based therapeutics.

Establish, implement, and improve pipelines for siRNA sequence design, integrating genomic, transcriptomic, and RNA structural data along with other factors that influence siRNA target specificity and efficacy. Build machine learning models based on high-throughput siRNA screening data to improve siRNA sequence selection. Collaborate with internal teams on NGS library preparation for off-target analysis. Interpret transcriptomic data to identify potential off-target signals and biological risk. Develop, maintain, and version-control reusable tools, scripts, and pipelines in Python, R, or other programming languages to support analysis, visualization, automation, and reporting. Generate clear and traceable data analysis reports, slide decks, and technical summaries. Collaborate with in vitro assay scientists, oligonucleotide scientists, and AI colleagues, and cross-functional partners to translate findings into actionable biological insights. Support transcriptomic and targeted validation studies relevant to seed-mediated and sequence-dependent off-target effects. Stay current on advances in RNAi biology, off-target analysis, transcriptomics, and computational methods relevant to nonclinical safety assessment.

BS, MS, or PhD in computational biology, genomics, RNA biology, or a related field with typically 0-4+ years (PhD), 8-10+ years (MS), or 10-12+ years (BS) of relevant biotech or pharmaceutical industry experience. Proficiency in Python and/or R, or other programming languages, with experience developing reproducible analytical workflows and analysis pipelines. Experience supporting off-target analysis, mechanistic interpretation, or risk assessment for siRNA, ASO, or related therapeutic modalities. Strong background in NGS library preparation technologies and transcriptomic data analysis. Strong understanding of molecular and cellular biology, with the ability to translate transcriptomic findings into biologically meaningful conclusions. Experience preparing scientific reports, presentations, or regulatory-facing documentation. Excellent communication, organizational, and collaboration skills. Ability to work independently, manage multiple priorities, and contribute effectively in a cross-functional environment.

Experience with transcriptome and other multi-omics data integration. Experience with RNA biology, RNAi mechanisms, or nucleic acid therapeutics. Prior experience evaluating machine learning-enabled scientific workflows, including LLM-assisted or agentic tools. Experience in pharmaceutical, biotech, or translational research settings. Position will be filled