Associate Principal / Senior Clinical Genomics Scientist, Centre for Genomics Research
Cambridge , United Kingdom | AstraZeneca
Industry:Pharmaceutical / Biotech
Job Description:69 people have viewed this job
AstraZeneca is a science-led, global biopharmaceutical company, with a focused portfolio in core therapies. We are committed to improving the health and lives of people across the globe, through our research and development platform, and a growing late-stage pipeline. Working in over 100 countries, we have strength in emerging markets, and our medicines are used by millions of patients and clinicians worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality.
AstraZeneca’s Centre for Genomics Research (CGR) is now looking for a Clinical Genomics Scientist to support the growing data, informatics and analytics capabilities of the Centre. The Centre was launched in April 2016 and has the bold ambition to analyse up to two million genomes by 2026. Using genomics data and state-of-the-art methods for genomic analysis, CGR will investigate underlying genetic causes of disease and integrate genomics across the drug discovery and development platform. This work is fundamental to driving the discovery of precision medicines.
In this role, you will contribute expertise in clinical data standards, medical terminologies, controlled vocabularies and ontologies used in healthcare data to facilitate deep phenotyping applied research at the forefront of statistical genetics and machine learning. You help the team coordinate the genomic analysis of large, complex and multidimensional data (e.g., rare-variant collapsing analyses, Mendelian randomisation and advanced analytics), and will use data mining to define groups of phenotypic features (smart phenotypes) to identify patient populations of interest to researchers.
You will bring expertise in clinical data ontologies and data formats, and their practical application. You will also contribute to the design of processes to ingest and interrogate patient-level clinical data, including electronic health records, clinical trials data and biomarkers.
Contribute to integration of clinical data with genomics data to support target discovery, validation, and genotype-phenotype associations enabling patient clinical trial stratification.
Helping to choose appropriate approaches, data management, analytical and statistical design to meet study objectives
Develop data dictionaries as required
Explaining the significance of data in a way that can be easily understood by others
Interpreting, integrating and reporting of a range of data, intelligence and evidence from large phenotype datasets.
Lead analyses of patient-level data, using machine learning techniques, for identification and characterisation of patient subgroups defined by clinical features and/or disease trajectories.
Ensuring own work, and work of team, is compliant with Good Clinical Practice, Safety, Health and Environment standards and all other internal standards and external regulations
Background in population genomics and/or applied clinical science
Previous experience handling and analysing patients’ data in a regulated, clinical research environment (incl. but not limited to EU privacy regulations, informed consent, Good Clinical Practice).
Previous exposure to clinical data standards, medical terminologies and controlled vocabularies used in healthcare data and ontologies (exposure to Electronic Health Record, ICD9/10 coding).
Ability to prioritize, problem-solve and perform difficult tasks
Proficiently communicate with team members and non-experts, both verbally and through documentation.
Clinical research experience in a health-related field.
Expertise with clinical data standards, medical terminologies and controlled vocabularies used in healthcare data and ontologies (ICD9/10; SNOMED; HPO; NHS digital).
Experience in handling longitudinal data at the individual patient level.
Experience working with health data models (e.g. OMOP CDM)
Hands-on experience of developing phenotypic cohort expressions in multi-disciplinary environments
Experience with Electronic Health Record (e.g. HES) data mining, handling and analysis.
PhD degree (or equivalent experience) in a biomedical informatics field of research.
Experience with extraction, de-identification, labelling, and curation of datasets for advanced analytics.
Experience in handling and analysing free text health records, including developing NLP pipeline, data/text mining, and clinical decision support.
Experience running the integration of ontologies with NLP engines.
Experience in advanced visualisation and visual analytics of healthcare data.
Consistent track record in delivering cutting edge informatics solutions in pharmaceutical drug discovery and development programmes
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