{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Applied AI Data Scientist”, “description”: “
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)
Company: Pharmacy2U | Certified B Corp
Location: Leeds
Job Description:
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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Posted: May 20th, 2026