Segmentation and targeting strategies
Engagement effectiveness and persistence insights
Trigger/alert and identification models
Oversee predictive modeling, machine learning, and causal measurement approaches.
Ensure robust validation, lift assessment, and experimental rigor.
Challenge model assumptions and outputs to maintain scientific credibility.
Measurement & Commercial Effectiveness
Establish structured measurement frameworks for campaign and engagement impact.
Guide test-and-learn approaches, including holdouts and quasi-experimental designs.
Align measurement outputs with broader commercial performance metrics and business KPIs.
Operationalization & Governance
Partner with engineering and platform teams to deploy and monitor models.
Ensure documentation, reproducibility, and governance standards are met.
Implement monitoring strategies for model performance and lifecycle management.
Team Leadership
Lead and mentor a team of data scientists aligned to the TA.
Set standards for analytical rigor, business relevance, and delivery excellence.
Build a culture of accountability, ownership, and continuous improvement.
What Success Looks Like
A clear, impact-driven analytics roadmap tied to commercial outcomes.
Analytics integrated into targeting, engagement, and investment decisions.
Strong trust with GCCI and TA leadership.
A high-performing data science team delivering consistent business value.
Measurable improvement in commercial effectiveness and ROI.
Basic Qualifications
10–15 years of experience in data science, advanced analytics, or commercial analytics leadership.
Demonstrated experience driving analytics delivery in pharmaceutical, healthcare, or other commercial industries (e.g., retail, consumer, financial services, tech).
Strong experience in predictive modeling, machine learning, and/or causal inference (hands-on or through technical oversight).
Experience translating complex analytical work into actionable business decisions.
Proven experience leading analytics teams and managing stakeholder expectations.
Proficiency in Python and SQL; experience with modern data platforms (e.g., Databricks, cloud-based environments) is preferred.
Preferred Qualifications
Experience working with healthcare or real-world longitudinal datasets (claims, EMR, specialty pharmacy).
Exposure to experimental design, uplift modeling, or structured test-and-learn frameworks.
Experience operationalizing models into production environments.
Strong executive communication and stakeholder influence skills.
Experience owning analytics roadmaps for brands, portfolios, or business units
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