Computational Pathology Scientist (CosMx SMI/Python/R Programming)

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 Job Type: Contract Pay: $76.00 - $78.00 per hour Expected hours: 40 per week Benefits: Health insurance Education: Bachelor's (Preferred) Experience: high-plex image analysis: 3 years (Preferred) Python: 3 years (Preferred) R programming: 3 years (Preferred) CosMx SMI: 1 year (Preferred) analyzing spatial transcriptomics and proteomics data: 3 years (Preferred) Work Location: Remote