About the role

Applied AI Data Scientist designing and building machine learning models for Pharmacy2U's medication management products. Collaborating with clinical stakeholders and engineering partners in a hybrid working environment.

Responsibilities

Design, build, validate, and document machine-learning models for medication behaviour, including adherence risk and medication synchronisation
Engineer temporal and behavioural features from prescription ordering patterns, cycle data, and adherence signals
Apply rigorous evaluation approaches, including cross-validation, calibration analysis, and fairness assessment across patient cohorts
Analyse large-scale medication ordering data to identify opportunities for new or improved AI-driven capabilities
Assess and communicate the clinical and commercial value of modelling approaches to support prioritisation and business cases
Collaborate with clinical stakeholders to define safety rules, constraints, and appropriate model usage in patient-facing contexts
Work with MLOps and engineering partners to package and deploy models into production environments (e.g. Azure ML)
Define and support model monitoring, including performance baselines, drift detection, and retraining criteria

Demonstrated experience applying machine learning techniques, including classification, regression, and ensemble methods (e.g. XGBoost, LightGBM, random forests)
Proficiency in Python for applied ML and analysis (pandas, scikit-learn, NumPy, matplotlib/seaborn)
Experience engineering features from temporal, behavioural, or sequential data
Comfortable using SQL to explore and extract data from large relational databases
Experience working with large-scale tabular datasets, including millions of records
Working knowledge of model interpretability and explainability techniques (e.g. SHAP, feature importance)
Experience with robust model evaluation practices, including cross-validation, calibration, class imbalance, and metrics beyond accuracy (precision, recall, F1, AUC)
Ability to communicate technical results clearly to non-technical stakeholders and document models for reuse and production
Background in applied data science or machine learning roles, with familiarity with regulated or healthcare contexts, cloud ML platforms, survival/time-to-event methods, and collaborative development practices (desirable)