Overview
We are looking for a dedicated and ambitious data scientist to continue our work in developing AI / informatics approaches to efficiently utilise real world clinical data for drug safety science (pharmacovigilance). Our previous work has shown that SAVSNET veterinary free-text clinical narratives are a source of real-world pharmacovigilance data. Here we will extend this work by using neural language models such as bidirectional encoder representations using transformers (BERT) models (including our own PetBERT model) to classify large volumes of records in order to determine if clusters of adverse events can be identified and then used in pharmaco-epidemiological studies.
Separately, we will pilot an assessment of whether association between relevant charged items and recorded events that may indicate an adverse event (for example, a change in prescription from Lokivetmab (Cytopoint) to Oclacitinib (Apoquel) or vice versa signifying a possible lack of efficacy adverse event).
Responsibilities
* Develop and apply AI / informatics approaches to real-world clinical data for drug safety science (pharmacovigilance).
* Utilise neural language models (including PetBERT) to classify large volumes of veterinary clinical records to identify clusters of adverse events for pharmaco-epidemiological studies.
* Pilot analyses to assess associations between prescribing changes and adverse event signals (e.g., Lokivetmab to Oclacitinib and vice versa).
* Collaborate with SAVSNET and relevant stakeholders to implement scalable data pipelines and reproducible analyses.
Qualifications
* Doctoral (or near completion) or Masters degree or equivalent experience (Grade 7), or BSc (Grade 6) in applied/medical statistics, health data sciences, health informatics, computer science and/or quantitative epidemiology.
* Professional experience in pharmacovigilance is desirable but not essential.
Details
* The post is available for up to 15 months with a preferred start date of 1 October 2025.
* If you are still awaiting your PhD to be awarded you will be appointed at Grade 6, spine point 30. Upon written confirmation that you have been awarded your PhD, your salary will be increased to Grade 7, spine point 31.
Commitment to Diversity
The University of Liverpool is committed to enhancing workforce diversity. We actively seek to attract, develop, and retain colleagues with diverse backgrounds and perspectives. We welcome applications from all genders/gender identities, Black, Asian, or Minority Ethnic backgrounds, individuals living with a disability, and members of the LGBTQIA+ community.
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