Design and implement computational pipelines for the analysis of large-scale biological datasets, including but not limited to genomics, transcriptomics, proteomics, and metabolomics.
Develop statistical methods to identify novel drug targets and predict drug responses.
Collaborate with cross-functional teams, including experimental biologists and machine learning scientists to advance drug discovery projects.
Stay abreast of the latest developments in computational biology, machine learning, and bioinformatics to ensure the incorporation of best practices into our research.
Contribute to the development and optimization of algorithms for data analysis and visualization.
Participate in the interpretation and reporting of results to stakeholders.
Requirements & Skills:
PhD in computational biology, human genetics, statistics, bioinformatics, computer science, or another quantitative discipline; or equivalent industrial experience.
Demonstrable track record of delivering complex data science-driven projects.
Proficiency in software development using Python, with upwards of 2 years of industry experience.
Experience in at least one of the key domain areas of statistics, human genetics and omics.
Familiarity with modern software engineering practices, collaborative tools, and CI/CD.
Openness to engaging in machine learning projects and a willingness to develop skills in this area as needed.
Robust understanding of advanced statistical techniques applicable to human biology, particularly in therapeutic development.
In-depth knowledge of statistical human genetics with the ability to creatively apply principles in target identification and validation.
Competency in statistical programming (e.g. Python, R) sufficient to enable large-scale genomic data analysis
Experience working with databases and data types related to target identification and validation, including pathways, phenotypes, ontologies, chemical entities, clinical health records.
Expertise in handling broad omics data types, including but not limited to single-cell transcriptomics, human genetics, and epigenetics data.
Proficiency in data mining and/or management of large datasets.
