Senior Analyst, US Commercialization Decision Intelligence & KDA Solutions

Roles & Responsibilities 

Analytics Delivery & AI Enablement 

• Hands-on build: Develop, prototype, and code analytical models, datasets, and automation workflows—translating business questions into analytical approaches, executing analyses, and synthesizing findings into actionable recommendations. 

• Platform contribution: Contribute reusable components (dashboards, always-on insights, scenario/measurement pipelines) to team self-service analytics hubs such as the Agentic MMx / Always-On Insights (AOI) platform, maximizing reuse and reducing manual effort. 

Agile Delivery & Project Management 

• Sprint-based delivery: Work within an agile, sprint-based development cycle—participating in sprint planning, backlog refinement, daily stand-ups, reviews, and retrospectives—to deliver analytical and AI product features iteratively and predictably.  

• Story ownership: Break down requirements into well-defined user stories, tasks, and acceptance criteria; provide effort estimates and track progress using collaboration and backlog tools (e.g., Jira, Azure DevOps).  

• Delivery coordination: Manage the end-to-end delivery of assigned workstreams—tracking timelines, dependencies, risks, and blockers, and proactively communicating status to the Manager/Team Lead and stakeholders.  

• Release readiness: Support release planning and deployment activities, ensuring features are demo-ready, documented, and aligned to the definition of done. 

Quality Assurance, Testing & UAT 

• Quality ownership: Take strong ownership of quality across the analytics and AI product lifecycle—embedding validation, peer code reviews, and best-practice standards into everyday delivery.  

• Testing: Design and execute test plans, test cases, and validation checks (data quality, logic, model output, and reconciliation), including unit, integration, and regression testing of analytical assets and pipelines.  

• UAT: Plan, coordinate, and support User Acceptance Testing with business stakeholders—preparing UAT scripts and test data, triaging and resolving defects, capturing sign-offs, and ensuring solutions meet business requirements before go-live.  

• Documentation & traceability: Maintain clear documentation, defect logs, and traceability from requirements through testing to release, ensuring reproducibility and auditability 

Agentic & AI Capability Development 

• Support the design, development, and testing of autonomous and semi-autonomous analytics agents using multi-agent frameworks, helping progress from descriptive analytics to causal analysis, root-cause insights, and predictive recommendations. 

• Contribute to the AI product lifecycle—proof-of-concept, pilot, and rollout—while following governance standards for safety, security, ethics, and privacy. 

• Apply and help operationalize LLMs for commercial use cases such as knowledge retrieval, summarization, generative analytics, and automation of insight generation. 

Stakeholder Partnership & Strategic Support 

• Partner closely with US Commercial stakeholders, Global Analytics, OCx, Marketing, and BI&T to understand business needs and contribute to solution design. 

• Act as a trusted analytical partner—clearly explaining insights, assumptions, and limitations, and supporting decision-making discussions. 

• Support prioritization of business requests by providing effort estimates, impact assessments, and analytical recommendations. 

• Contribute to stakeholder presentations, readouts, and working sessions with clear, structured storytelling. 

Technical Execution, Governance & Data Stewardship 

• Build and maintain analytical assets including datasets, models, dashboards, and automation workflows. 

• Work closely with BI&T and data engineering teams to troubleshoot data issues and ensure reliable, timely, and scalable data availability. 

• Ensure analytical outputs are reproducible, well-documented, explainable, and aligned with data/AI governance and compliance standards—applying privacy-by-design and human-in-the-loop practices where required. 

Required Qualifications 

Education & Experience 

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

• 3+ years of professional experience in advanced analytics, decision science, or AI-driven roles. 

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

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

• Experience in pharmaceutical, biotech, or healthcare industries preferred; familiarity with pharmaceutical data (claims, APLD, specialty pharmacy, digital signals, promotional data) is a plus. 

• Understanding of how data, analytics, and AI can be applied to solve commercial business problems. 

Core Competencies 

• Strong written and verbal communication skills, with the ability to translate complex analytics into clear business insights. 

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

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

• Hands-on expertise in applied statistics, analytics, and AI/ML techniques. 

• 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. 

Technical Skills (Preferred) 

• Predictive and statistical analytics using Python and/or R; exposure to AI/ML and text analytics (e.g., NLP, clustering, propensity models, uplift modeling) and to LLMs. 

• Exposure to causal inference and incrementality methods (geo-experiments, matched markets, uplift modeling); awareness of MMx/adstock/response-curve concepts is a plus. 

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

• Experience working in collaborative analytics environments (e.g., Databricks, SharePoint, Git-based workflows); familiarity with cloud analytics platforms (Snowflake, Spark) is a plus. 

• Familiarity with omnichannel, digital marketing, and commercial data sources, including CRM (e.g., Veeva) and multi-touch attribution (MTA). 

• Awareness of AI governance and regulatory standards (model monitoring, privacy, human-in-the-loop; HIPAA, GDPR/CCPA) in regulated environments.