About the role

Commercial Data Scientist designing and owning models for customer segmentation at Pharmacy2U. Collaborating with marketing and commercial teams to present actionable data insights.

Responsibilities

Build, validate, and maintain machine learning models for customer segmentation, propensity scoring, churn prediction, and marketing attribution
Design and engineer features using transactional data, CRM engagement, website behaviour, and product usage signals
Lead A/B testing and test‑and‑learn experimentation to measure model‑driven commercial impact
Quantify impact in commercial terms, including revenue uplift, churn reduction, campaign ROI, and customer lifetime value
Act as the primary data science partner to Marketing and Commercial teams, shaping priorities and analytical direction
Present insights and strategic recommendations directly to senior leadership with confidence and autonomy
Translate complex analytical outputs into clear commercial narratives and actionable recommendations
Partner with CRM teams to operationalise model outputs through marketing automation platforms
Define scoring cadences, audience refresh cycles, and CRM integration touchpoints
Embed data science and experimentation best practice across the commercial organisation

Demonstrated applied machine learning expertise across classification, clustering, regression, and propensity modelling
Proven experience building customer segmentation models and translating outputs into actionable commercial strategies
Strong Python skills (pandas, scikit‑learn, NumPy, matplotlib/seaborn) and the ability to write SQL against large relational datasets
Expertise in A/B testing, experiment design, and measuring incrementality, uplift, and ROI
Ability to frame analytical work in clear commercial terms, linking models to revenue, margin, retention, CLV, and cost‑to‑serve
Strong data visualisation, storytelling, and communication skills, with confidence presenting to senior leadership
Extensive experience as a data scientist in a commercial, retail, or marketing analytics environment
Hands-on experience working with CRM and customer journey data, and operationalising models for campaign targeting and activation
Familiarity with modern analytics tooling and platforms, including Git, cloud ML environments, and CRM/marketing automation platforms