We are seeking Data Scientists to support the implementation of Health Assessment Outcomes and AI Personalisation Projects. These initiatives are part of a multi-year programme aimed at delivering measurable impact through advanced clinical statistical techniques and innovative AI solutions.
The successful candidates will evaluate and quantify the impact of Health Assessments on customer health and wellbeing, while deepening the understanding of customer profiles, health determinants, and personalised healthcare solutions.
We are looking for individuals with applied experience in data science within a healthcare or clinical setting, who can also act as subject matter experts by collaborating closely with clinicians, operational teams, and business managers.
Ensure the accuracy, relevance, and robustness of clinical outcomes data through rigorous validation methodologies.
Identify, define, and track supplementary metrics to enhance outcomes analysis.
Collaborate with stakeholders to agree on analytical assumptions and clearly articulate limitations.
Lead the development of research protocols, outlining objectives, scope, and methodological frameworks.
Conduct in-depth analysis using appropriate statistical and computational techniques.
Contribute to a comprehensive white paper in collaboration with academic partners.
Evaluate and tailor clinical risk models to align with datasets and clinical objectives.
Map input variables to model requirements, ensuring semantic and structural consistency.
Conduct validation and performance testing, including clinical utility assessments.
Collaborate with clinical experts to review model assumptions and implications for patient care.
Prepare documentation for clinical governance bodies to ensure compliance with ethical and regulatory standards.
Integrate validated clinical risk models into AI personalisation algorithms.
Design and execute pilot studies using real-world patient cohorts.
Collaborate with clinical leadership to review pilot outcomes and refine approaches.
Define clinical rules and inclusion/exclusion criteria to guide model application.
Enhance AI-driven personalisation by integrating updated clinical risk insights.
Applied experience in Data Science within a healthcare or clinical setting.
Expertise in medical statistics, epidemiology, and population health.
Proficiency in study design, statistical modelling (e.g., survival analysis, regression techniques), and longitudinal data analysis.
