Senior Analyst, US Commercialization

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 BIT 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 BIT 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)

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