The Molecular Profiling and Data Science group is looking for a computational scientist to lead discovery efforts in cardiometabolic disease, and to pioneer analytical approaches for using data generated by omics and other technologies. This highly collaborative role encompasses translational, pre-clinical, and technology development projects, working with scientists and clinicians throughout the company. The successful applicant will have strong computational skills, can clearly communicate with data science experts and non-experts alike, and is motivated to learn relevant disease biology and contribute to charting research direction.

As a Sr. Scientist, Bioinformatics, a day might include the following:

• Lead efforts to discover disease mechanism, biomarkers, and drug targets for cardiac or renal diseases from large patient cohorts profiled with RNA-Seq, proteomics, digital histopathology, and exome-seq.

• Collaborate with cardiovascular scientists to design the most informative experiment, and analyze the resulting data (single-cell, spatial transcriptomics, bulk RNA-Seq, others) to inform disease and target biology.

• Develop algorithms to interpret omics data from in vitro models such as organoid and tissue-on-chip.

• Identify cell type specific genes or splice isoforms to enable targeted delivery of genetic medicines.

• Work with wet lab scientists in our group to benchmark new omics technologies, and introduce relevant analytical approaches.

• Create tools to enable large data queries, e.g. using LLMs.

• Publish and present novel findings in patents, conferences, and peer-reviewed journals.

This job might be for you if you:

• Are driven by the opportunity to make an impact on patients.

• Have strong computational skills, and enjoy the challenge of extracting biological meaning from rich data sets.

• Can effectively communicate with collaborators from various backgrounds and value teamwork.

• Have curiosity that extends to both high-level and detail questions.

• Want opportunities to showcase and publish your work.

To be considered for this role, you must have a PhD degree in Bioinformatics, Data Science, or similar quantitative field, with post-graduate experience preferred. You must have experience in analysis, interpretation, and visualization of omics data using R or Python, and have a strong foundation of statistical data analysis. A solid understanding of genetics and cell biology is very important; background in cardiac or renal biology would be a big plus. Hands-on experience with machine learning, predictive modeling, and/or image analysis is highly desired.

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