Senior Research Scientist, Centre for Genomics Research
|Posting date:||14 November 2019|
|Closing date:||12 December 2019|
|Location:||Cambridgeshire, Cambridgeshire, SG8 6EE|
Senior Research Scientist
Location: Melbourn Science Park, Cambridge, UK
Salary: Competitive + Excellent Benefits
At AstraZeneca, we turn ideas into life changing medicines. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality.
AstraZeneca's Centre for Genomics Research (CGR) are looking for an outstanding Senior Bioinformatics Data Scientist to contribute to the genomic strategies of the CGR in an unprecedented genomic initiative, involving the analysis of up to 2 million genomes. This includes analysing the sequences of participants from AstraZeneca clinical trials and the sequence data from large population cohorts including the ~500,000 UK Biobank participants.
Genomics is fundamental to our laboratory research, our clinical trials and the launch of new precision medicines. Our in-house clinico-genomic database is already among one of the largest globally, currently housing hundreds of thousands of human exome and genome sequences, enabling AstraZeneca to identify genetic determinants for disease risk, validate new targets for medicines and improve patient stratification opportunities among core therapeutic areas of oncology, respiratory, cardiovascular, renal and metabolic disease.
The Senior Research Scientist will work together with leaders in human genomics, developing statistical genetic approaches for application on large-scale genomics datasets and delivering innovative science to genome analytics. Within the CGR they will be responsible for continuing the success of the genomics team in designing and implementing novel machine learning and deep learning methods applied to the area of genomics, including the collection of 500,000 genomes from the UK Biobank. This includes translating structured or unstructured, complex genomic data into the appropriate research problem, model and analytical solutions with support from a dynamic team in CGR's multidisciplinary genomics research environment comprising of computational biologists, population geneticists, software engineers, statistical geneticists, postdoctoral researchers and clinician experts. You will also work closely with experts in translational science, drug discovery, pre-clinical modelling, and clinical development; thus, contributing to the broader objectives and success of the company-wide Genomics Initiative.
- Design and implement novel machine learning methods applied to Genomics problems
- Extract research and/or business value from highly unstructured genomic data and metadata, including the ~500,000 UK Biobank resource
- Support large scale data preparation, the optimisation of analytics platforms and the industrialisation of proven analytics methods
- Coordinate and execute genomic analyses within AstraZeneca's Centre for Genomics Research
- Assess the scientific & technical integrity of algorithms and tools within the analysis pipeline
- Maintaining a well-developed knowledge of genomic science and technical advances in the international community
- Deliver novel insights into the biology of disease, validation of new targets for medicines and the improvement of selection of patients for clinical trials
- Lead genomic science input into the drug development process
- Present novel results to top tier Genetics and/or machine learning conferences and publish in high impact journals
- Collaborate to apply genomic analysis with discovery and development teams
- Communicate results to a variety of audiences, technical and non-technical
- Ensuring own work, and work of team, is compliant with Good Laboratory Practice, Safety, Health and Environment standards and all other internal AstraZeneca standards and external regulations
Qualifications, Skills and Experience Required:
- PhD degree (or equivalent experience) in Computational Biology, Bioinformatics, Statistical Genetics, Biostatistics, or a related quantitative discipline
- Solid experience in developing learning methodologies and building robust machine learning systems
- Experience in large-scale data analysis and applied statistics in Genomics
- Strong programming skills. Solid experience in one or more languages (Python, R, C++) and in open-source ML packages (e.g. scikit-learn, TensorFlow/Keras/PyTorch)
- Strong knowledge of algorithms and data structures
- Ability to communicate effectively with team members and non-experts, both verbally and through documentation
- High level understanding or interest in the potential of genomics to impact drug discovery
- Ability to prioritize, problem-solve and perform difficult tasks while under pressure
- Ability to carry out duties under minimal supervision
- Excellent interpersonal skills and willingness to work within a team in a quickly evolving environment
- Track record of peer-reviewed publications in high-level scientific journals
- Passion for applying machine learning into the life sciences domain
- Previous experience in a similar role
- Experience in high performance and/or cloud computing
- Experience in case-control sequencing based statistical analyses
- Experience quantifying and interpreting the clinical relevance of rare variants
- Familiarity working on genomics studies involving one of AstraZeneca's core therapeutic areas
If you are interested, please apply.
Applications Open 14th November 19
Applications Close 31st December 19
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.