Data Scientist
- 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.
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