AI Data Scientist, Deciphex

• PhD in data science, bioinformatics, computational biology, biomedical science, statistics, computer science, or a related quantitative field (equivalent research experience may also be considered)
• Strong proficiency in Python and relevant machine-learning and scientific-computing frameworks, such as PyTorch and 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 selection of clinically or operationally meaningful performance measures
• Experience designing and conducting deep-learning experiments, including model training, evaluation, ablation studies, and systematic comparison of modelling approaches
• Ability to translate scientific questions into well-defined datasets, analytical plans, validation strategies, and clear evidence-based conclusions
• Experience developing reproducible analytical workflows using appropriate practices for version control, testing, environment and configuration management, provenance, and technical documentation
• Ability to read and critically interpret scientific publications, technical standards, and relevant regulatory guidance
• Excellent written and verbal communication in English, including the ability to produce clear technical and scientific documentation for specialist and non-specialist audiences
• Demonstrated ability to work effectively across multidisciplinary and multi-organisation teams, managing competing scientific and delivery priorities

Desirable

• As a Senior AI Data Scientist in Research Pathology at Deciphex, you will lead advanced data-science research supporting the development and validation of AI for toxicologic pathology and translational research. Working at the interface of histopathology, computational pathology, and machine learning, you will translate complex scientific questions into rigorous datasets, experiments, benchmarking frameworks, and qualified analytical evidence
• Working closely with pathologists, AI researchers, software engineers, and product teams, you will ensure that model development is grounded in real tissue morphology, diagnostic reasoning, and unmet research needs. You will also support translational research activities, including biomarker discovery, IHC quantification, and tissue-based endpoint characterisation, and help convert exploratory research into reproducible methods, scientific publications, evidence packages, and future product capabilities
• You will play a leading role in pathology foundation-model research, designing training experiments, developing practice-relevant benchmarks, and translating exploratory research into reproducible methods and downstream pathology applications
• 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 for downstream pathology applications
• 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, including non-clinical benchmarks grounded in whole-slide pathology use where no public standard exists
• 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
• Collaborate with the AI, software, and product teams to align research outputs with Deciphex's scientific and product roadmap