RCI-ABBV-32947 Computational Biologist / Bioinformatics Scientist (Spatial Transcriptomics/Proteomics/Digital Pathology /Image Analysis/CosMx/Python/HALO)

• *Title: Scientist III, Computational Pathology • *Remote role • *Spatial Biology group • *Candidate will do data analysis of generated spatial proteomics and transcriptomic data using PhenoCycler Fusion and CosMx • *90% computational task and 10% pathology based work with scientist to interpret data • *Bachelors degree with more than 10 yrs exp will be considered if they have spatial transcriptomic data analysis and CosMx experience • *Computational biologist who are familiar with looking at pathology images would also work and able to contribute the interpretations (Must have understanding of pathology and biology of disease) • Python and R programming exp (must have) • Work on existing pipelines and work on building pipeline • Computational biology experience • CosMx SMI (proteomics data analysis) experience (Must have) • Exp in high-plex PhenoCycler Fusion (CODEX) • Data analysis experience needed (must) • 1 year of CosMx data analysis exp (Must have) • spatial transcriptomic data analysis exp is needed (must have) • Generate spatial proteomics and transcriptomic data using PhenoCycler Fusion and CosMx • Data sets: Spatial transcriptomic and CosMx data sets on diff disease types • Halo, Visiopharm, QuPath exp is nice to have • *Must have: • 1 year of CosMx data analysis exp (Must have) • Python and R programming exp (must have) • CosMx SMI (proteomics data analysis) experience (Must have) • Spatial transcriptomic data analysis exp is needed (must have) • *Purpose: • The Precision Medicine Pathology team drives the scientific strategy for translational tissue-based biomarker development & discovery target validation/MOA projects, leads pathology collaborative programs, conducts histopathological evaluation & analysis of IHC & spatial biology technologies, and provides technical/scientific leadership to histotechnicians & pathology scientists. • The successful candidate will have advanced knowledge and experience analyzing spatial transcriptomics and proteomics data generated on the CosMx SMI and PhenoCycler Fusion platforms. • *Responsibilities: • Implementation of different scripts and pipelines for spatial transcriptomics data analysis and analysis of high-plex PhenoCycler Fusion (CODEX) images. • Independently performing end-to-end high-plex image analysis (tissue classification, cell segmentation, detection of marker positivity, cell phenotyping, unsupervised clustering, neighborhood analysis, proximity analysis). • Acting as a subject matter resource and training other team members in spatial analysis tasks. • Collaborating with pathologists and digital pathology scientists to support spatial biology projects. • Presenting the results and findings from spatial biology studies to stakeholders. • *Qualifications:** • MSc, PhD, or equivalent degree in biological sciences / computational biology / engineering / computer science / informatics • Fluency in Python and R • Experience in implementing and utilizing open-source scripts and pipelines for high-plex image analysis • Experience in quantitative digital pathology analysis platforms such as Halo, Visiopharm, QuPath • Close familiarity with tissue microscopic anatomy and histology (normal and diseased) is a plus • Excellent verbal communication skills are required including the demonstrated ability to effectively and clearly summarize results for presentation and report generation • Strong motivation, attention to detail, ability to think independently and fully integrate into a high achieving team environment • Ability to multi-task and manage multiple projects