JOB DESCRIPTION:

We seek a talented and highly motivated Data Scientist to join our expanding Spatial omics team within the Discovery Immunology Pathology group. The successful candidate will develop and apply computational methods for the analysis of image-based spatial omics datasets, including the integration of multi-modal omics data and data interpretation in order to derive Client biological insights, identify disease biomarkers and new therapeutic targets. The successful candidate will be part of a unique multidisciplinary team working closely with pathologists, image analysts, and research scientists across the organization in a highly dynamic and collaborative environment to support pathology driven spatial omics projects that contribute to innovative drug discovery research.

Responsibilities:

• Design, develop and implement computational pipelines for processing and analyzing spatial omics datasets, including spatial transcriptomics and proteomics.
• Collaborate with pathologists and image analysts to develop methods to integrate and analyze spatial transcriptomics data with other spatial modalities, such as IHC or mIF.
• Collaborate with pathologists to validate spatial analysis tools in the context of tissue structure to ensure accuracy and reproducibility.
• Ensure scalability and efficiency of the pipelines to handle large and complex datasets.
• Lead the development of data visualization tools to effectively communicate findings to both technical and non-technical stakeholders.
• Stay current with emerging trends and technologies in spatial omics and bioinformatics and assess their applicability to ongoing projects.
• Generate concise reports and accurately document project details, algorithms and results in electronic laboratory notebook.

Requirements:

• Ph.D. (or equivalent experience) in Computational Biology, Bioinformatics, Systems Biology, or a related field. Background in life sciences or experience in the pharmaceutical industry preferred.
• Strong proficiency in programming languages such as Python, R, or MATLAB, and relevant data science libraries (such as NumPy, Pandas, Matplotlib, Scikit-learn, etc.) for data analysis, manipulation, and visualization.
• Experience with bioinformatics tools and databases, especially those pertinent to omics data analysis such as Squidgy or Scampi.
• Excellent analytical, problem-solving, and communication skills.
• Ability to work independently and as part of a team, managing multiple projects simultaneously.
• Familiarity with digital pathology image analysis platforms (Vis pharm and Qu path) and spatial omics analytic platforms (Bio Turing).
• Experience with machine learning frameworks and data visualization tools.
• Familiarity with cloud-based (AWS) and high-performance computing environments.

• Design, develop and implement computational pipelines for processing and analyzing spatial omics datasets, including spatial transcriptomics and proteomics.
• Collaborate with pathologists and image analysts to develop methods to integrate and analyze spatial transcriptomics data with other spatial modalities, such as IHC or mIF.
• Collaborate with pathologists to validate spatial analysis tools in the context of tissue structure to ensure accuracy and reproducibility.
• Ensure scalability and efficiency of the pipelines to handle large and complex datasets.
• Lead the development of data visualization tools to effectively communicate findings to both technical and non-technical stakeholders.
• Stay current with emerging trends and technologies in spatial omics and bioinformatics and assess their applicability to ongoing projects.
• Generate concise reports and accurately document project details, algorithms and results in electronic laboratory notebook