Senior AI Data Scientist
New
D
DeciphexDigital Pathology
Work from home in Ireland.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Required Skills
- PythonMachine LearningPyTorchData scienceDeep Learningscikit-learn
Requirements
- PhD in data science, bioinformatics, computational biology, biomedical science, statistics, computer science, or a related quantitative field (equivalent research experience considered)
- Strong proficiency in Python and relevant machine-learning frameworks (e.g., PyTorch, scikit-learn)
- Experience curating, integrating, and quality-controlling large imaging datasets and associated structured metadata
- Demonstrable experience applying data science and machine learning to digital pathology, biomedical imaging, or complex biomedical datasets
- Strong grounding in applied statistics and model validation, including experimental design and performance measures
- Experience designing and conducting deep-learning experiments, including training, evaluation, and systematic comparison of modelling approaches
- Ability to translate scientific questions into well-defined datasets, analytical plans, and clear evidence-based conclusions
- Experience developing reproducible analytical workflows using version control, testing, and configuration management
- Ability to read and critically interpret scientific publications, technical standards, and regulatory guidance
- Excellent written and verbal communication in English
- Demonstrated ability to work effectively across multidisciplinary and multi-organisation teams
Responsibilities
- Curate, characterise, and analyse large preclinical and translational pathology datasets, including whole-slide images, structured study data, annotations, and associated metadata
- Lead the design and execution of advanced data-science and machine-learning research across toxicologic pathology and translational research applications
- Design and conduct pathology foundation-model training experiments, including data-curation studies, training-recipe ablations, fine-tuning, and evaluation of learned representations
- Validate downstream pathology AI models through performance characterisation, confounder analysis, and evidence packages suitable for regulatory scrutiny
- Build benchmarking datasets and practice-relevant evaluation tasks for foundation and downstream models
- Develop and standardise data-processing pipelines and validation routines that support model development, benchmarking, and deployment
- Support translational research applications, including biomarker discovery, IHC quantification, and tissue-based endpoint characterisation
- Work directly with pathologists to ground datasets and model outputs in real morphological findings, lesion terminology, and diagnostic reasoning
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