[Hiring] Project Data Scientist @Deciphex

Role Description

As a Project Data Scientist in Research Pathology at Deciphex, you will prepare, characterise, and validate the datasets and model outputs that drive our research pathology AI across toxicologic pathology and translational research. Working at the interface of histopathology, computational pathology, and data science, you will turn whole-slide images and structured study data into curated, statistically characterised datasets and qualified analytical outputs. This will include:

• Control-dataset characterisation
• Confounder and batch-effect detection
• Data-quality assessment
• Validation of downstream AI models

You will support the evaluation of pathology AI models and downstream applications such as anomaly detection, lesion identification, segmentation, and whole-slide classification. This will include:

• Contributing to practice-relevant benchmarks
• Characterising model performance and failure modes
• Identifying confounders and batch effects
• Applying clinically and operationally meaningful measures of performance

You will work alongside pathologists, the AI team, software and product teams, contributing to validation strategy and ensuring that datasets and analytical outputs meet the evidentiary and regulatory expectations of preclinical and translational research.

A major focus of the role will be supporting Deciphex’s delivery within two EU-funded public-private partnerships: BIGPICTURE (IMI) and VICT3R (IHI). You will contribute to:

• Shared data models
• Metadata standards
• Dataset onboarding
• Control-data analysis
• Interoperability between Deciphex platforms and consortium infrastructure

Working across internal teams and external partner organisations, you will help resolve data and transfer issues and ensure that project outputs meet agreed standards for quality, traceability, and responsible data handling.

Qualifications

• Advanced degree in data science, bioinformatics, computational biology, biomedical science, biostatistics, statistics, computer science, or a related quantitative field, or equivalent applied experience
• Proficiency in statistical computing and data manipulation using one or more relevant languages and tools
• Experience applying data science or machine learning to biomedical imaging, life-science, or other complex scientific datasets
• Experience analysing and evaluating machine-learning model outputs, including performance characterisation, error analysis, and interpretation of results in a scientific or biomedical context
• Experience curating, integrating, quality-controlling, and documenting datasets and associated structured metadata
• Strong grounding in applied statistics, including exploratory analysis, experimental design, identification of confounders and batch effects, and selection of meaningful performance measures
• Ability to translate scientific and project requirements into well-defined datasets, analytical workflows, quality-control checks, and clear evidence-based outputs
• Experience developing reproducible analytical workflows using version control, testing, environment and configuration management, data provenance, and technical documentation
• Excellent written and verbal communication in English, including the ability to produce clear technical documentation and communicate data issues to specialist and non-specialist stakeholders
• Demonstrated ability to work effectively across multidisciplinary and multi-organisation teams, managing competing scientific and delivery priorities

Requirements

• Understanding of preclinical toxicology study design, treatment and control groups, endpoints, and histopathology workflows
• Familiarity with non-clinical data standards and regulated research environments, including SEND/CDISC terminology, GLP principles, or regulatory-grade whole-slide image management
• Familiarity with whole-slide imaging, digital pathology platforms, image formats, and image-data management
• Prior experience contributing to EU-funded consortia (e.g. IMI, IHI, Horizon Europe) or other multi-organisation scientific partnerships involving industry, academia, CROs, or public bodies
• Understanding of biomedical data governance, including data integrity, traceability, responsible data handling, and GDPR principles
• Ability to read and interpret scientific publications, technical standards, data specifications, and relevant regulatory guidance

Benefits

• Healthcare benefits
• Competitive annual leave
• A true sense of meaning in your work by contributing to better patient outcomes
• The opportunity to work alongside a world-class high performing team in a hyper-growth startup environment
• A chance to work on exciting, challenging and unique projects
• Regular performance feedback and significant career growth opportunities
• A highly collaborative and supportive multicultural team