Head of Data Science

Qureight is a company focused on accelerating clinical trials for lung and heart diseases through their AI-powered data and imaging curation platform. They are seeking a Head of Data Science to lead the data science function, ensuring that machine learning outputs are clinically interpretable and valuable for clinical trials and drug development.

Responsibilities:

• Define the long-term data science strategy aligned with company objectives
• Lead and scale the data science function:
• Build and lead a high-performing data science team across model analysis, client analysis, and advanced methodologies
• Define and embed best practices for reproducible, modular, and scalable data science pipelines
• Set standards for statistical validity, analytical rigor, and reproducibility of outputs across the organisation
• Ensure strong alignment with machine learning, data engineering, and clinical teams
• Translate ML outputs into trial-ready products:
• Drive development of data science outputs that support commercial offerings and client value propositions
• Own the transformation of machine learning outputs into clinical trial endpoints, biomarker definitions, and statistical outputs used by sponsors
• Ensure outputs are scientifically valid, clinically interpretable, and suitable for regulatory use and use by sponsors here instead
• Develop advanced analytical methodologies:
• Lead development of methodologies including synthetic control arms, longitudinal modelling, and patient-level outcome modelling
• Ensure methods are robust, reproducible, and defensible in external settings
• Drive scientific outputs and validation:
• Oversee delivery of validation reports, abstracts and publications, and sponsor-facing analyses
• Ensure data science outputs reinforce Qureight’s scientific credibility
• Represent the data science function in external scientific and commercial engagement
• Partner across the organisation:
• Work closely with ML, data engineering, clinical/translational, product, and commercial teams
• Align data science outputs with productization and customer needs
• Act as a key bridge between technical teams and external stakeholders
• Support trial delivery and sponsor engagement:
• Contribute to trial design and endpoint strategy
• Translate complex outputs into clear, actionable insights