Principal Scientist, Bioinformatics - Translational Medicine

at  Autolus Ltd

Role Summary
With an expanding Translational Medicine team, an exciting opportunity has arisen for a Lead or Principle Scientist in Bioinformatics and Data Management to join the team to support global expansion of our cell and gene therapy products.
We are seeking a highly skilled and motivated Bioinformatician to lead the development and delivery of integrated analyses including but not limited to Whole Exome Sequencing (WES), RNA sequencing (RNAseq), clinico-demographic, and biomarker data from patients treated with CAR-T therapy from Autolus’ pipeline. The successful candidate will possess a strong statistical background and lead our multi-omics strategy, manage the integration, and analysis of multi-omics data and contribute significantly to the scientific publication process.

Key Responsibilities Data Integration and Management:

• Lead the integration of WES, RNAseq, clinico-demographic, and biomarker data from clinical trials and research studies.
• Develop and implement pipelines and workflows for data processing and integration, ensuring high-quality data management and consistency.
• Maintain and update databases and data management tools, ensuring data accuracy, security, and compliance with regulatory standards. Bioinformatics Analysis:
• Perform comprehensive bioinformatics analyses, including variant calling, copy number change, differential expression analysis, pathway analysis, and biomarker discovery in the context of the clinical environment.
• Utilize statistical and computational tools to interpret multi-omics data and generate meaningful medical and biological insights.
• Develop and validate predictive models for patient outcomes to CAR-T therapy based on integrated data. Collaboration and Communication:
• Work closely with cross-functional teams, including clinical, R&D and biostatistics groups, to support data needs and project objectives.
• Provide bioinformatics expertise and guidance to collaborators, ensuring effective data usage and interpretation.
• Collaborate with external partners and vendors as necessary to enhance data integration and analysis capabilities. Scientific Publications:
• Lead the preparation of scientific manuscripts and reports based on integrated data analyses.
• Contribute to the writing, editing, and submission of scientific publications in high-impact journals.
• Present findings at scientific conferences, meetings, and internal forums to communicate research outcomes and advance the understanding of CAR-T therapy. Tool Development and Innovation:
• Develop custom scripts, tools, and software applications to support data integration, analysis, and visualization.
• Stay abreast of the latest bioinformatics methodologies, technologies, and industry trends to drive innovation and improve data integration processes. Quality Control and Documentation:
• Ensure quality control of bioinformatics analyses and results, adhering to best practices and standard operating procedures.
• Document all processes, analyses, and results thoroughly, providing clear and detailed records for reproducibility and regulatory compliance.

Experience

• Minimum of 3 years of post-doctoral experience in bioinformatics, with a focus on multi-omics data analysis in a clinical or translational research setting. (E)
• Exceptional proficiency in statistical analysis and modelling, with a deep understanding of advanced statistical concepts and their applications in translational or clinical biology. (E)
• Extensive experience with next-generation sequencing data analysis, including WES and RNAseq. (E)
• Comprehensive understanding of industry standard bioinformatics tools, databases, and best practices. (E)
• Experience in applying best practices for data management, version control, and reproducible research. (E)
• Implement industry standard methods and best practices for data processing, analysis, and interpretation.

Preferred Qualifications
Ph.D. or Master’s degree in Bioinformatics, Computational Biology, Genomics, or a related field.(E

• Lead the integration of WES, RNAseq, clinico-demographic, and biomarker data from clinical trials and research studies.
• Develop and implement pipelines and workflows for data processing and integration, ensuring high-quality data management and consistency.
• Maintain and update databases and data management tools, ensuring data accuracy, security, and compliance with regulatory standards. Bioinformatics Analysis:
• Perform comprehensive bioinformatics analyses, including variant calling, copy number change, differential expression analysis, pathway analysis, and biomarker discovery in the context of the clinical environment.
• Utilize statistical and computational tools to interpret multi-omics data and generate meaningful medical and biological insights.
• Develop and validate predictive models for patient outcomes to CAR-T therapy based on integrated data. Collaboration and Communication:
• Work closely with cross-functional teams, including clinical, R&D and biostatistics groups, to support data needs and project objectives.
• Provide bioinformatics expertise and guidance to collaborators, ensuring effective data usage and interpretation.
• Collaborate with external partners and vendors as necessary to enhance data integration and analysis capabilities. Scientific Publications:
• Lead the preparation of scientific manuscripts and reports based on integrated data analyses.
• Contribute to the writing, editing, and submission of scientific publications in high-impact journals.
• Present findings at scientific conferences, meetings, and internal forums to communicate research outcomes and advance the understanding of CAR-T therapy. Tool Development and Innovation:
• Develop custom scripts, tools, and software applications to support data integration, analysis, and visualization.
• Stay abreast of the latest bioinformatics methodologies, technologies, and industry trends to drive innovation and improve data integration processes. Quality Control and Documentation:
• Ensure quality control of bioinformatics analyses and results, adhering to best practices and standard operating procedures.
• Document all processes, analyses, and results thoroughly, providing clear and detailed records for reproducibility and regulatory compliance