Overview

Our client is seeking a Data Scientist and CRM professional to design and build predictive models that enable hyper-personalized patient engagement and drive targeted remarketing initiatives. This role will focus on leveraging patient history and engagement data to generate actionable insights, recommend next-best actions, and improve patient retention, service uptake, and overall campaign effectiveness.

The ideal candidate will combine strong machine learning and statistical modelling expertise with a practical business mindset, translating complex analytical outputs in a dynamic working environment into commercially impactful recommendations and marketing actions.

Key Responsibilities

• Analyze patient history and engagement data to identify behavioural patterns, trends, and opportunities for improved patient engagement.
• Develop predictive models and recommendation engines to support next-best-action (NBA) decision-making and targeted remarketing campaigns.
• Build and validate models for key use cases, including patient reactivation, churn prediction, service uptake propensity, and cross-sell/upsell opportunities.
• Segment patient populations using advanced analytics such as clustering, propensity modelling, and predictive scoring.
• Partner with marketing, operations, and clinic teams to translate model outputs into actionable campaigns and engagement strategies.
• Design and implement testing frameworks (such as A/B testing) to measure campaign effectiveness and optimize outcomes.
• Develop dashboards, reports, or lightweight tools to operationalize model outputs for business stakeholders.
• Ensure data quality, model governance, and compliance with healthcare data privacy requirements.
• Continuously monitor model performance and recommend enhancements to improve business impact.

Ideal Background

• Proven experience in Data Science, Machine Learning, Advanced Analytics, or Applied Statistics.
• Strong track record of developing and deploying predictive models for customer, patient, marketing, or commercial use cases.
• Experience working with PII, customer journey, CRM, patient, or behavioral datasets.
• Ability to translate complex analytical findings into practical business recommendations and actions.
• Experience working in fast-paced environments with a strong focus on execution and measurable outcomes.
• Comfortable working cross-functionally alongside marketing, operations, and clinical teams
• Healthcare, life sciences, or patient analytics experience is highly advantageous.

Key Skills

• Predictive Analytics & Machine Learning
• Customer / Patient Segmentation
• Propensity Modelling & Recommendation Engines
• Churn Prediction & Reactivation Modelling
• Marketing Analytics & Campaign Optimization
• A/B Testing & Experimentation Design
• Python, R, SQL
• Data Visualization & Dashboarding
• Statistical Analysis & Model Validation
• Business Insight Generation

Preferred Experience

• Experience working with healthcare data, EMRs, patient journeys, or treatment pathways.
• Familiarity with healthcare data privacy, governance, and compliance requirements.
• Experience supporting CRM, marketing automation, customer engagement, or personalization initiatives.
• Knowledge of recommendation systems, next-best-action frameworks, and customer lifecycle analytics.

Success Measures

• Accuracy and performance of predictive models developed.
• Improvement in campaign conversion, engagement, and patient reactivation rates.
• Number of next-best-action recommendations successfully deployed.
• Speed of moving models from development into operational use.
• Demonstrated uplifts in revenue, patient engagement, and/or commercial outcomes.