Head of Lead ID

Role Summary

Executive Director, RNA Team (Head of Lead ID). This leadership role will scale how we identify and select lead oligonucleotide therapeutics and create datasets that fuel AI/ML modelling efforts. The role will build and scale enterprise discovery platforms within the RNA therapeutics team that integrate computational biology, high-throughput screening, multi-omic analytics, and machine learning to accelerate target-to-candidate timelines while improving probability of technical and regulatory success. This leader will guide teams establishing quantitative, high-throughput discovery systems enabling earlier go/no-go decisions and generating differentiated molecular candidates across 20+ active RNA programs.

Responsibilities

• Lead an established team of BSc to PhD-level scientists and engineers to deliver enterprise-scale lead discovery capabilities
• Lead team adaptation and execution of high-throughput transcriptome-wide selectivity profiling methods (e.g., concentration-response digital gene expression) to quantify hybridization-dependent and hybridization-independent off-targets with improved sensitivity
• Partner with teams developing predictive ML models for in vivo activity, pharmacodynamics, and tolerability from sequence, structure, and chemical modification features
• Partner with automation leadership to deploy high-throughput screening at scale, targeting thousands of molecules per program
• Work closely with data Science/AI/ML teams to integrate lead discovery data into unified siRNA modeling platforms that merge molecular design with oligonucleotide chemistry
• Collaborate with chemistry teams on structure-activity relationship studies, including novel chemistries
• Collaborate with Biology leads across therapeutic areas to ensure fit-for-purpose evidence packages for progression decisions
• Mentor engineering efforts to build robust pipelines processing large-scale transcriptome datasets, centralizing curated data assets for reusable analytics
• Establish data governance frameworks ensuring molecule discovery data is database-ready and integrated with enterprise systems
• Manage NGS core operations or equivalent high-throughput assay infrastructure; scale throughput to meet portfolio demand

Qualifications

• Ph.D. in Computational Biology, Bioinformatics, Molecular Biology, or related field
• 12+ years of experience in drug discovery, with significant depth in RNA-targeted therapeutics (ASO, siRNA, splice-switching oligonucleotides)
• 5+ years of leadership experience managing PhD-level scientists and/or engineers in both “wet” and “dry” science teams

Skills

• Demonstrated track record of building and scaling discovery platforms that have advanced multiple candidates into clinical development
• Deep knowledge of oligonucleotide chemistry, including modified nucleotides, backbone modifications, and conjugate strategies and screening processes
• Deep expertise in transcriptomics, including bulk and single-cell RNA-seq design, analysis, and interpretation
• Experience with mechanism-of-action and pharmacodynamic studies using multi-omic approaches
• Proficiency in machine learning methods (XGBoost, Random Forest, neural networks) applied to biological sequence data
• Experience with high-throughput NGS assay development and core operations
• Familiarity with ETL pipelines, cloud-scale computation, and reproducible analytics workflows
• Ability to operate across subject areas and translate complex technical concepts into strategic recommendations
• Track record of cross-functional collaboration with chemistry, biology, and clinical teams
• Experience presenting at industry conferences and contributing to peer-reviewed publications
• Demonstrated ability to make data-driven decisions under uncertainty and manage risk effectively