Hybrid (onsite 3 days a week/ 2 days WFH). Hypothesize, analyze, visualize & interpret complex large multi-dimensional data. Curate data to train & validate pathology machine learning models. Architect, develop & maintain digital pathology infrastructure. Manage, quality check, organize digital pathology data. Optimize, automate & document workflows to improve efficiency. Provide guidance & training to fellow colleagues.
Requirements:
Must have a Bachelor's degree in Computer Science, Electrical Engineering, Biomedical Engineering, Industrial Engineering, Bioinformatics, Mathematics, Physics, or a related field, & 1 year programming in Python, Matlab &/or R.
Of experience required, must have 1 year in each of the following:
(i) visualizing large multi-dimensional data using the following 2 Python packages: seaborn & matplotlib;
(ii) using either JPM or PRISM statistical software to perform statistical analysis on large datasets;
(iii) using at least 1 of the following: Python (including scipy, scikit-learn), MATLAB, or R statistical programming language to perform statistical analysis on large datasets;
(iv) building machine learning models including ensemble models & kernel-based models;
(v) utilizing dimensionality reduction techniques including PCA, LDA, SVD & self-organizing maps;
(vi) applying all of the above to pathology images & associated metadata.
Of experience required, must have 6 months of experience in:
(i) using 1 of the following digital pathology tools: Visiopharm, Halo, QuPath or Napari;
(ii) utilizing AWS s3 buckets & automating using script integration.
Work experience may be gained concurrently.
Application Instructions:
Apply online at https://careers.abbvie.com/en or send resume to Job.opportunity.abbvie@abbvie.com. Refer to Req ID: REF43648D.
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