Our client is seeking a Director of Bioinformatics Engineering to lead their engineering efforts in building and maintaining data analysis pipelines.
This role is central to their efforts in integrating human genetics, multi-omics data analysis with practical applications in biology and clinical settings. You will have a deep understanding of bioinformatics, systems engineering, data science, and human genetics!
Key Responsibilities:
Lead and oversee bioinformatics/data scientist consultants in the development of computational tools and algorithms for the processing and analysis of complex biological data, with a focus on human genetics.
Interface directly with biology, protein engineering, and clinical teams to translate data insights into actionable clinical and therapeutic interventions, leveraging insights from human genetics.
Oversee the integration and customization of data analysis platforms to fit the specific needs of different research and clinical projects, ensuring that genetic data is effectively integrated.
Stay at the forefront of developments in bioinformatics, pipeline architecture, omics technologies, and human genetics to ensure the adoption of cutting-edge practices within the team.
Design, develop, and maintain scalable data pipelines for the analysis of large-scale omics datasets, including genomics, proteomics, metabolomics, and human genetics data.
Engineer and optimize computational frameworks to support the data needs of internal biology, protein engineering, and clinical teams, focusing particularly on genetic data integration.
Qualifications:
Ph.D. (or equal work experience) in Bioinformatics, Computational Biology, Computer Science, or a related field with a strong emphasis on data engineering, bioinformatics pipeline development, and human genetics.
Advanced programming skills in Python, R, and/or other relevant languages; experience with software development best practices and version control systems.
Demonstrated expertise in the architecture and deployment of high-throughput data processing pipelines and the integration of multiple types of omics data, including extensive experience with genetic data.
Strong collaborative and communication skills, with the ability to engage effectively with multidisciplinary teams and stakeholders at all levels.
Experience in the application of machine learning techniques to large biological datasets, with a particular focus on genetic data, is highly desirable.
Experience in managing bioinformatics or computational biology projects, preferably within a biotechnology or pharmaceutical setting.