We have an opportunity available for a Senior/Research Scientist, Translational Bioinformatics to join our Research team located at Bio21 Institute. In this position, you will provide scientific and technical expertise to help answer biomedical questions at the pre-clinical and clinical stage of drug development using advanced bioinformatic methods. You will expand CSL’s research capability by supporting the Global Translational Science group in order to derive biological insights from in-house high-throughput sequencing data and large-scale genomic data available in public and commercial databases. Furthermore, you will devise an integrative approach to analyze multiple types of data obtained from in-house and external resources in order to answer key questions in the areas of biomarker discovery and understanding of mechanisms of action of targets, drug candidates and diseases of interest.
Working closely with the Data Science Group, the Translational Science Group and other Research, you will set project objectives and determine and implement data generation, analysis and software 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 strategies for bioinformatics methods.
**Reporting into the** **Senior Manager Bioinformatics & AI in the Global Research Data Science Group, you will be responsible to:**
+ Provide input into the **design of next-generation sequencing experiments** including RNA-seq, DNA-seq, Rep-seq, etc.
+ Carry out **bioinformatic analyses** on datasets generated in-house from different NGS and array experiments or collected from external databases.
+ Contribute to the **interpretation and reporting** of the biological insights derived from data analytics based on GSP guidelines.
+ Work with the Translational Science group to establish an in-house capability to **utilize genetic and genomic variants** in order to improve biomarker discovery, understanding of MoA, patient stratification strategies and prioritisation of indications for drug targets.
+ Work with the Translational Science group to **benchmark and adapt new sequencing technologies** (e.g. long read and single cell sequencing) in order to answer challenging research questions across multiple drug discovery projects.
+ Work with the Translational Science group to **integrate data from external databases** (e.g., GTEx, ExAC, ENCODE, UK Biobank) into CSL Research computational platforms and improve upon current data mining practices.
+ Work with in-house software developers to develop fully **automated workflows** that help accelerate Translational Science data processing activities.
+ Work with Research and IT data managers in order to **devise new processes for managing translational data** and ensure compliance with CSL’s regulatory requirements (e.g., Good Scientific Practices, etc.).
+ Contribute to **CSL’s growing collaboration network** with academic laboratories and industry partners locally and globally.
**To be successful in this role, you will have:**
+ PhD in computational biology, bioinformatics, computer science, statistical genetics, or a related discipline with significant computational and biomedical components coupled with Bachelor’s or Master’s degree in biological sciences
+ 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 in analysing RNASeq datasets and working with life scientists to draw statistically solid biological insights from data.
+ Experience in genomic variant analysis on patient cohorts and/or utilizing genomic variant data from public databases to understand mechanism of action of targets, drug candidates or diseases and support translational biomarkers.
+ Experience in applying network biology and pathway analysis algorithms (using open source and/or commercial tools, e.g. IPA).
+ Experience in using public databases to answer key research questions related to experimental design, biomarker discovery and patient stratification (e.g., gene expression databases, clinical variants, etc.).
+ Experience in working in a Unix-like and high-performance computing (HPC) environment using Python, R and other scientific computing languages.
+ High quality track record of publications and presentations at national and international conferences.
Applications must address the selection criteria above and include a current CV and covering letter.
**Applications close June 7, 2002**
Employment screening, including criminal history, medical assessment, confirmation of experience and stated qualifications, Australian working rights and other relevant checks form part of the candidate suitability
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