Senior Data Scientist - Machine Learning & AI in Genomics
Location: Barcelona, Spain
Competitive Salary & Benefits
Meaningful Impact: Make a more meaningful contribution to impact patients' lives every day.
Join the team where you are empowered to follow the science. This is the place for curious minds to dig deep into the biology of complex diseases and uncover breakthroughs.
What you'll do:
We have an opportunity for a talented Senior Data Scientist at the Centre for Genomics Research (CGR) to be part of ground-breaking research at the forefront of human genomics. You will be part of a dynamic team in CGR's multidisciplinary genomics research environment comprising bioinformaticians, computational biologists, genome scientists, software engineers, postdoctoral researchers, and disease area specialists. You will also work closely with specialists in translational science, drug discovery, pre-clinical modelling, and clinical development. In this role, you will be responsible for designing and implementing novel machine learning and deep learning methods applied to genomics. This includes identifying research problems that could be addressed through structured or unstructured, complex genomic data and developing appropriate models and analytical solutions.
Design and implement novel machine learning and deep learning methods for variant interpretation and other genomics research questions.
Extract research and/or business value from highly unstructured genomic data and metadata, including the 500,000 UK Biobank resource.
Work with engineering and architecture to support large scale data preparation, the optimisation of analytics platforms, and the industrialisation of proven analytics methods.
Coordinate and execute analyses within AstraZeneca's Centre for Genomics Research.
Deliver novel insights into the biology of disease, including complex diseases and rare diseases.
Develop methods for validation of new targets for medicines and the improvement of selection of patients for clinical trials.
Assess the scientific & technical integrity of algorithms and tools within the analysis pipeline.
Essential Criteria:
PhD degree (or equivalent experience) in Computational Biology, Bioinformatics, Machine Learning, or a related quantitative discipline.
Solid experience in developing learning methodologies and building robust production machine learning systems.
Strong programming skills and knowledge of algorithms and data structures.
Solid experience in one or more languages (Python, R, C++) and in open-source ML packages (e.g. scikit-learn, PyTorch, TensorFlow, Keras).
Applied experience with deep learning models (such as CNNs and Transformers).
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 and problem-solve.
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 to the life sciences domain.
Desirable Criteria:
Experience in large-scale data analysis in genomics and/or other omics.
Experience with Knowledge Graphs and Graph Neural Networks.
Familiarity with Generative AI (LLMs).
Experience in Unix environments and Bash scripting.
Experience in high performance and/or cloud computing.
If your passion is science and you want to become part of a team that makes a bigger impact on patients' lives, then there's no better place to be. Here we truly understand science and apply it every day to strengthen and grow our pipeline!
Date Posted: 06-sept-2024
Closing Date: 19-sept-2024
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds 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.
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