Associate Director, Customer Experience Measurements & Insights

Summary

• Lead enterprise-wide measurement, experimentation, and optimization across personal and non-personal engagement, focusing on media and campaign optimization.
• Establish standardized measurement frameworks, KPI governance, and closed-loop insight generation to improve customer experience, orchestration performance, and business outcomes.

• Build standardized measurement frameworks and campaign intelligence (historical performance + decision science outputs like MMx/KDA).
• Define and govern enterprise KPIs, measurement logic, metric definitions, and reporting standards.
• Oversee monitoring of engagement “health” (reach, frequency, engagement, opt-outs, execution signals) and deliver early deviation/opportunity insights.
• Design and operationalize test-and-learn/experimentation (A/B tests, lift studies, incrementality, matched controls) and translate results into scale/refine/stop decisions.
• Lead Omnichannel Orchestration Engine measurement and always-on closed-loop optimization across personal and non-personal channels.
• Measure content/message performance (adoption, freshness, affinity, fatigue, suitability) and evaluate AI-driven personalization/NBA impact.
• Partner across marketing/analytics/media/CRM/field BI&T to enable measurement adoption and faster learning; provide executive dashboards and decision frameworks.

• Pilot and scale advanced analytics; enable responsible LLM use with governance, explainability, and compliance.

• Bachelor’s in CS/Data Science/Analytics/Statistics/Economics or related quantitative field.
• 6+ years in omnichannel engagement measurement/advanced analytics/commercial insights in pharma.
• Strong HCP/patient journey understanding (channel interactions, sequencing, pacing, message effectiveness).
• Experience designing measurement frameworks, KPI standardization, governance.
• Expertise in impact measurement (attribution, experimentation/incrementality, causal inference).
• Python or R for analysis/modeling.

• ML techniques (embedding-based clustering) for channel/journey/content insights.
• LLMs for insight/enablement; validate AI insights with rigor.
• SQL and ML modeling in Python (e.g., pandas, Num Py, stats models, causal libraries).

Princeton, NJ, US: $167,540–$203,013.