Sr. Analyst, US Customer Engagement Measurement & Insights

Roles & Responsibilities 

Customer Engagement Measurement & Analytics Delivery 

• Independently delivered measurement analyses assessing the effectiveness of omnichannel customer engagement across field, CRM, paid media, owned digital, and third-party channels. 

• Apply machine learning techniques to identify patterns in customer engagement, channel synergy, sequencing, pacing, and message performance. 

• Support the development and operationalization of measurement frameworks, KPI tracking tools, and performance dashboards aligned with the team's strategic priorities. 

• Synthesize analytical findings into clear, structured outputs and actionable recommendations for internal stakeholders and team leads. 

Experimentation & Test-and-Learn Support 

• Support the design and execution of AI-informed test-and-learn strategies, including A/B tests, lift studies, and incrementality experiments across customer engagement tactics. 

• Assist in defining success metrics, measurement windows, and statistical approaches to evaluate new engagement strategies. 

• Contribute to the documentation and socialization of experimental learnings to inform ongoing campaign planning across brands. 

• Help refine experimentation frameworks to accelerate learning cycles and improve engagement effectiveness. 

Advanced Analytics & Attribution 

• Apply advanced statistical and AI/ML techniques to measure the incremental impact of customer engagement activities. 

• Support the use of attribution frameworks such as multi-touch attribution, Markov chain modelling, and causal inference methods (e.g., propensity scoring, matched control). 

• Contribute to the integration of outputs from MMx, KDA, and predictive models to support engagement optimization strategies. 

• Leverage automation to improve speed, consistency, and repeatability of insights delivery. 

Stakeholder Partnership & Communication 

• Partner with US Commercial stakeholders, Brand, Omnichannel Strategy, Field Analytics, CRM, Digital, and Technology teams to contribute to AI-enabled measurement solution design. 

• Clearly communicate measurement methodologies, assumptions, model limitations, and implications for decision-making to both technical and non-technical audiences. 

• Contribute to stakeholder presentations and working sessions with structured, insight-driven storytelling focused on impact and commercial actions. 

Team Collaboration & Capability Building 

• Collaborate within cross-functional and global teams to ensure consistent, scalable, and governed analytical delivery aligned with AI best practices. 

• Mentor junior analysts on advanced measurement methodologies, analytical rigor, and responsible data usage. 

• Contribute to the development of reusable analytics frameworks, measurement templates, and best practices that scale across brands. 

• Promote high standards of reproducibility and documentation across all team deliverables. 

Technical Execution & Data Stewardship 

• Build and maintain AI-enabled measurement datasets, dashboards, and automated workflows supporting omnichannel engagement analytics. 

• Work closely with data engineering, technology, and build teams to troubleshoot data issues and provide measurement-driven feedback to enhance commercial solutions. 

• Ensure analytical outputs are reproducible, well-documented, and aligned with data governance, compliance, and responsible use standards. 

Skills & Competencies 

• Strong written and verbal communication skills with the ability to translate complex analytics and engagement insights into clear business outcomes, including AI-generated findings. 

• Solid project execution and organizational skills, with the ability to manage multiple analytical workstreams in parallel. 

• Strong analytical thinking and problem-solving skills, with attention to detail, data quality, and measurement rigor. 

• Hands-on expertise in applied statistics, analytics, and advanced measurement concepts relevant to omnichannel performance. 

• Familiarity with HCP and patient engagement journeys across digital, field, and media channels. 

• Collaborative mindset with the ability to work effectively in a matrixed, stakeholder-driven environment. 

• Curiosity and passion for learning, innovation, and continuous improvement in analytics and measurement. 

Experience & Qualifications 

• BA/BS required; advanced degree preferred in data science, analytics, statistics, mathematics, economics, life sciences, computer science, or engineering. 

• 3–5 years of professional experience in advanced analytics, decision science, or omnichannel engagement measurement roles, ideally within an AI-driven commercial environment. 

• Proven experience delivering end-to-end analytics and measurement projects, from problem framing to insight delivery. 

• Experience with attribution and impact measurement techniques (e.g., multi-touch attribution, causal inference, uplift modeling) is a strong plus. 

• Demonstrated ability to partner with business stakeholders and support data-informed decision-making. 

• Experience in pharmaceutical, biotech, or healthcare industries preferred. 

• Proficiency in Python or R for analytical modeling and data transformation. 

• Experience with cloud-based analytics environments (Databricks, Snowflake, or equivalent) preferred. 

Technical Skills (Preferred) 

• Predictive and statistical analytics using Python and SQL, including uplift modeling, drivers analysis, propensity modeling, and causal inference methods. 

• Familiarity with attribution frameworks such as multi-touch attribution and Markov chain modeling. 

• Experience working with performance metrics, model diagnostics, and AI-enabled analytics outputs. 

• Data visualization and dashboarding tools (e.g., PowerBI, Tableau, Dash, or similar platforms). 

• Experience in collaborative analytics environments (e.g., Databricks, Git-based workflows, SharePoint). 

• Familiarity with omnichannel, CRM, digital marketing, and commercial data sources. 

• Practical exposure to LLMs and generative AI as an insight and enablement layer, including prompt design for classification, summarization, and insight generation.