1. Purpose of the role:Execute Target Discovery and Validation activities within the Data Science team to identify and validate cell-state driving disease-modifying lncRNAs and regulatory elements by integrating multi-omics transcriptomics and epigenomics datasets, with main focus on immunology and inflammatory indications
2. Accountabilities- Preprocess and analyze raw multi-omics datasets (including bulk/single-cell/single-nuclei RNA-seq, ATAC-seq, PRO-seq, CUT&RUN, 3D genomics) to characterize the regulatory genome driving cell states and extract the features to perform target identification.
- Integrate human genomic, genetic, and epigenetic datasets (including GWAS, eQTLs, and chromatin conformation annotations) with internal transcriptomic pipelines to identify potent cell-state driving lncRNA targets.
- Translate high-dimensional omics analysis and results into clear, biologically grounded scientific presentations and documentation to facilitate strategic alignment, influence multi-disciplinary program stakeholders, and support executive decisions.
Requirements3. Required Knowledge and ExperienceEssential Qualifications & Technical Capabilities (Must-Haves)- PhD in Computational Biology, Bioinformatics, Computer Science, Genomics, or a related quantitative or life sciences field with a robust computational focus; or equivalent demonstrated capability and technical depth.
- Proficient programming skills in R or Python, supported by strong data hygiene, and a desire to build shared reproducible toolkits and pipelines.
- Experience on the identification of novel targets, demonstrating a strong drive to design and leverage computational workflows with minimal supervision.
- Deep technical proficiency in preprocessing, integrating, and analyzing multi-omics datasets, specifically bulk and single-cell/single-nuclei RNA-seq, ATAC-seq, PRO-seq, and CUT&RUN, to define cell states and identify biological drivers within the regulatory genome.
- Excellent scientific understanding of gene regulation, transcriptomics, and the non-coding genome (in particular lncRNAs) and gene regulatory networks.
- Proven capability in presenting complex multi-omics statistical analyses, and data science concepts in a clear, narrative-driven manner to both technical, biological, and non-expert stakeholders, serving as a bridge between dry and wet lab activities.
Desirable Experience & Specialized Expertise (Nice-to-Haves) - Biological expertise in immunology, inflammatory indications, or immune cell profiling, with the ability to contextualize transcriptomic discoveries in the frame of chronic pathology (highly desired).
- Work within scalable server and cloud automation standards, write reproducible scripting templates, and keeps data science analysis standards that raise the technological bar for the entire Data Science & AI department.
- Demonstrated history of fostering a collaborative scientific culture by providing code reviews, guiding junior analysts on data science best practices, and ensuring strict adherence to reproducible computational biology.
