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.
