Applied AI Data Scientist, Leeds

Role: Applied AI Data Scientist

Location: Leeds, LS15 8GB (hybrid schedule, 1–2 days a week in office)

Salary: £60,000–£75,000 per annum + up to a 10% annual discretionary bonus and extensive benefits

Contract type: Permanent

Employment type: Full time

Working hours: Monday–Friday, 37.5 hours per week. Core hours 09:30–16:00; flexible around those.

We are the nation’s largest online pharmacy, a market leader with 25 years of experience, supporting over 1.8 million patients in England with NHS prescriptions from request to delivery. A Great Place to Work certified organisation and a certified B Corp, we prioritise colleague experience, social and environmental responsibility and aim to be a world‑leading, patient‑centric digital healthcare provider.

Why you'll love working with us 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 & well‑being

• 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 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)

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