CSL has an exciting opportunity for a Senior Scientist, Translational Bioinformatics (Statistical Genetics) to join our team. In this newly created position, you will provide scientific and technical expertise to help answer biomedical research questions at the pre-clinical and clinical stage of drug development using advanced bioinformatics and human genetics methods.
You will expand CSL’s R&D capability in Translational Bioinformatics (with a focus on Statistical Genetics) in order to derive biological insights from large-scale genetic/genomic data available in public and commercial databases, as well as in-house high-throughput sequencing and other ‘omic experiments.
You will work closely with scientists within the Data Science Group, the Translational Science Group and other Research and Clinical Development groups to set project objectives and to determine and implement data generation, data analytics, and bioinformatics platform development strategies. You will also work alongside CSL’s external collaborators including spending time in their research laboratories as required to enable alignment of objectives and bioinformatics strategies.
Reporting to the Manager of Statistical Genetics, you will:
Design, implement, and interpret human genetics analyses to answer key questions around asset positioning, target validation, biomarker discovery, indication selection, patient stratification, and drug mechanism-of-action.
Provide input into experimental design of bioinformatics analyses including transcriptomics (RNA-seq), proteomics, and similar assays, performed either in-house or by collaborators.
Contribute to the interpretation and reporting of the biological insights derived from genetics / bioinformatics analyses, in collaboration with the Translational Science group and other subject-matter experts at CSL.
Work with in-house and external bioinformatics software developers to develop automated and reproducible analysis workflows.
Your experience:
PhD in statistical genetics, computational biology, bioinformatics, computer science, or a related discipline, with significant computational and biomedical components.
Bachelor’s or Master’s degree in biological sciences or related fields (e.g., genetics, computational biology, bioinformatics, molecular biology, genetics, etc.).
3+ years of post-doctoral experience in biomedical data analytics in a highly collaborative, interdisciplinary environment in academia and/or industry with a focus on translational research.
Experience working with a wide range of human genetics and ‘omic datasets to answer scientific questions related to human health, the ability to develop impactful research questions in these areas, and evidence of pursuing such projects to completion.
Experience working with and interpreting results from human genetics analyses such as genome-wide association studies (GWAS), Mendelian randomisation, phenome-wide association analyses, fine mapping, and colocalisation.
Experience in querying and interpreting results from public human genetics databases, such as Open Targets, UK Biobank PheWeb, FinnGen, GWAS Catalog, GTEx / FIVEx, EpiGraphDB, ClinVar, and others.
Experience in analysing RNA-seq datasets (e.g., using limma-voom) and working with life scientists to draw statistically solid biological insights from data.
Apply now to join a purpose-driven, values-based company committed to protecting human health.
Applications Close: 19 June 2022
PhD in statistical genetics, computational biology, bioinformatics, computer science, or a related discipline, with significant computational and biomedical components.
Bachelor’s or Master’s degree in biological sciences or related fields (e.g., genetics, computational biology, bioinformatics, molecular biology, genetics, etc.).
3+ years of post-doctoral experience in biomedical data analytics in a highly collaborative, interdisciplinary environment in academia and/or industry with a focus on translational research.
Online: www.linkedin.com/jobs/view/3100799934/