Bioinformatics/Data Scientist

1. What experience do you have working with large datasets and compute clusters?
2. Describe your experience building workflows/pipelines and/or using bioinformatics software and tools to analyze data.
3. What programming languages are you comfortable using for bioinformatics data analysis?
4. What bioinformatics areas are you familiar with? (e.g., single cell, bulk genomics, transcriptomics, epigenetics, flow, spatial, metagenomics, etc.)
5. What life science disciplines do you have experience in? (e.g., immunology, infectious disease, cancer research, etc.)
6. Provide examples of basic machine learning/AI concepts and/or how you've applied them in bioinformatics data analysis.

Overview

The Bioinformatics/Data Scientist will conduct comprehensive analyses of multi-omics data generated from organoid systems and corresponding normal tissues. This position is central to the SOM Center's research objectives, focusing on characterizing organoid fidelity, identifying biomarkers of successful differentiation, and developing computational frameworks for organoid quality assessment.

Responsibilities

• The successful candidate will analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from various organoid systems and their tissue counterparts.
• The position will develop and implement computational pipelines for data processing, quality control, and statistical analysis.
• A major component of the role involves integrating SOM-generated data with publicly available datasets to benchmark organoid characteristics against normal tissue profiles.
• The position requires close collaboration with experimental teams to interpret results and guide protocol optimization, as well as contributing to manuscript preparation and presenting findings at scientific conferences.

Required Qualifications

• Candidates must hold a PhD in bioinformatics, computational biology, biostatistics, or a related quantitative field.
• Extensive experience with single-cell data analysis, including familiarity with tools such as Seurat, Scanpy, or similar platforms, is essential.
• Strong programming skills in R and Python are required, along with experience in statistical analysis and data visualization.
• Knowledge of proteomics and metabolomics data analysis workflows is necessary.

Preferred Qualifications

• Previous experience analyzing organoid datasets is strongly preferred.
• Experience with machine learning approaches for biological data, familiarity with pathway analysis tools, and knowledge of developmental biology principles will be considered valuable assets.
• Experience with high-performance computing environments and version control systems is desirable.