AI Data Scientist

This is a fully remote position, open to applicants in Ireland.

📋 Description

• Lead sophisticated data-science research to support the development and validation of AI in toxicologic pathology and translational research.

• Transform intricate scientific inquiries into robust datasets, experiments, benchmarking frameworks, and validated analytical evidence.

• Curate, characterize, and analyze extensive preclinical and translational pathology datasets, encompassing whole-slide images, structured study data, annotations, and metadata.

• Direct advanced data-science and machine-learning research in applications related to toxicologic pathology and translational research.

• Design and execute pathology foundation-model training experiments, which include data-curation studies, training-recipe ablations, fine-tuning, and evaluation of learned representations.

• Validate downstream pathology AI models through performance characterization, confounder analysis, and evidence packages that meet regulatory standards.

• Develop benchmarking datasets and evaluation tasks relevant to practice for both foundation and downstream models.

• Create and standardize data-processing pipelines and validation routines that support model development, benchmarking, and deployment.

• Assist in biomarker discovery, IHC quantification, and tissue-based endpoint characterization.

• Collaborate directly with pathologists to ground datasets and model outputs in morphological findings, lesion terminology, and diagnostic reasoning.

• Work alongside AI, software, and product teams to ensure research outputs align with the scientific and product roadmap.

• Transform exploratory research into reproducible methods, scientific publications, evidence packages, and future product capabilities.

⛳️ Requirements

• PhD in data science, bioinformatics, computational biology, biomedical science, statistics, computer science, or a related quantitative discipline; equivalent research experience may also be considered.

• Strong expertise in Python and applicable machine-learning and scientific-computing frameworks, such as PyTorch and scikit-learn.

• Experience in curating, integrating, and quality-controlling large imaging datasets along with associated structured metadata.

• Proven experience applying data science and machine learning to digital pathology, biomedical imaging, or intricate biomedical datasets.

• Solid foundation in applied statistics and model validation, including experimental design and the selection of clinically or operationally significant performance measures.

• Experience in designing and conducting deep-learning experiments, which includes model training, evaluation, ablation studies, and systematic comparison of modeling strategies.

• Ability to convert scientific questions into well-defined datasets, analytical plans, validation strategies, and clear evidence-based conclusions.

• Experience in developing reproducible analytical workflows utilizing version control, testing, environment and configuration management, provenance, and technical documentation.

• Ability to read and critically assess scientific publications, technical standards, and relevant regulatory guidelines.

• Excellent written and verbal communication skills in English.

• Demonstrated capacity to work effectively across multidisciplinary and multi-organization teams while managing competing scientific and delivery priorities.

• Working knowledge of histopathology, including tissue morphology, pathological findings, and terminology.

• Understanding of preclinical toxicology study design, including treatment and control groups, endpoints, and histopathology workflows.

• Experience with whole-slide imaging, digital pathology platforms, image formats, annotation workflows, and large-scale image-data management.

• Familiarity with pathology foundation models, self-supervised learning, Vision Transformers, representation learning, model fine-tuning, or knowledge distillation.

• Experience in training and evaluating deep-learning models using GPU, HPC, cloud, or distributed-computing infrastructure.

• Experience with translational pathology applications such as biomarker discovery, IHC or multiplex-image analysis, tissue segmentation, and tissue-based endpoint characterization.

• Experience collaborating with software engineers to convert research models and analytical workflows into maintainable, tested, and product-ready implementations.

• Record of peer-reviewed publications, conference presentations, or substantial contributions to applied AI research.

• Must be eligible to work in Ireland; Irish work permits or visa sponsorship are not available.

🏝️ Benefits

• Healthcare benefits.

• Competitive annual leave.

• Opportunity to contribute to improved patient outcomes.

• Chance to work alongside a world-class, high-performing team in a rapidly growing startup environment.

• Opportunity to engage in exciting, challenging, and unique projects.

• Regular performance feedback.

• Significant opportunities for career growth.

• Highly collaborative and supportive multicultural team.

• Work-from-home arrangement in Ireland.