Applied AI Data Scientist

About the role

Role: Applied AI Data Scientist Location: Leeds, LS15 8GB We operate a hybrid schedule, 1-2 days a week in the office Salary: £60,000 - £75,000 per annum + up to a 10% annual discretionary bonus and our extensive benefits Contract type: Permanent Employment type: Full time Working hours: Monday - Friday 37.5 hours per week. We work on a core hours principle. Our core hours are 09:30 - 16:00; you can work around these to suit you! Do you want to work for the nation's largest online pharmacy ensuring excellence for all our patients? We're a market leader in the pharmacy world, with 25 years' experience, helping over 1.8 million patients in England manage their NHS prescriptions from request through to delivery. We are Great Place to Work certified as we consider colleague experience a top priority every day, and as a certified B Corp we also meet high standards of social and environmental responsibility. Our people are fundamental to our success and ensuring we achieve our vision to be a world leading, patient-centric digital healthcare provider. We are committed to continuing to develop a positive, open and honest working environment for all. Our tech teams keep us running 24/7 to make sure all our patients get world class service. To support that, this role may include participation in an out-of-hours rota as required by the business. We operate fair scheduling process as well as additional compensation for all on call periods. Build and operate the machine learning models underpinning Pharmacy2U's medication management products. Working with rich temporal and behavioural patient data, you will address problems with direct patient impact, including predicting medication need and identifying risk of non‑adherence. This is a hands‑on role within a small, high‑impact team, where models are productionised and embedded into real patient‑facing services. Why you'll love working with us We believe great people deserve great support. That's why we offer a benefits package designed to look after your health, finances, career and life outside work. Financial security & rewards - Competitive contributory pension - Occupational sick pay - Long-service awards and refer-a-friend bonuses - Professional registration fees covered (GPhC, NMC, CIPD and more) - Cycle to Work and Green Car schemes (subject to eligibility) Family-friendly - Enhanced maternity and paternity pay - Flexible hybrid working to help balance work and home life Health & wellbeing - Private healthcare insurance at discounted rates (Aviva) - Employee Assistance Programme and in-house mental health support - Access to discounted gym memberships via Blue Light Card and benefits schemes - Regular health and wellbeing initiatives Career growth - Strong commitment to CPD, training and professional development Time off & flexibility - 25 days' annual leave, increasing with service - Buy and sell holiday scheme Everyday perks & exclusive discounts - Blue Light Card and employee discount platform - Exclusive discounts at The Springs, Leeds - 25% off health & beauty purchases - 25% off Pharmacy2U Private Online Doctor services Culture & community - Regular social events throughout the year What you'll be doing? - 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 Who are we looking for? - 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 explain ability techniques (e.g. SHAP, feature importance) - Experience with robust model evaluation practices, including cross‑validation, calibration, class imbalance, and metrics beyond a

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