Data Intelligence and Advanced Analytics Manager

Job Description AA China continues to expand its commercial footprint and business complexity, creating increasing demand for data‑driven decision‑making, granular performance insights, and scalable analytical solutions. To support this evolution, AA China is strengthening its Data Intelligence & Transformation capability to better translate data into actionable business impact. The Advanced Analyst is a key role within the Data Intelligence & Transformation team, responsible for translating complex business questions into deployable analytical and intelligent solutions, and ensuring high‑quality delivery, adoption, and operational sustainability. Working under the guidance of the Data Intelligence & Transformation Lead, this role partners closely with cross‑functional stakeholders across Commercial, BTS, and other functions to support analytical solution delivery, user adoption, and continuous optimization, while ensuring compliance with data governance and security standards. KEY DUTIES AND RESPONSIBILITIES: Business solution deployment and analytical delivery Translate cross‑functional business needs into deployable, data‑driven and intelligent solutions, and ensure analytical models and outputs are effectively delivered, governed, and adopted by end users Analyze requirements from functions such as Sales, Marketing, AMI, ADI, and SFE, and convert business questions into clear analytical scenarios, assumptions, and specifications. Work with stakeholders to clarify use cases, success measures, and data inputs, and contribute to clear and auditable requirement documentation. Execute analytical and intelligent application development activities under defined frameworks, including data preparation, model building, testing, validation, and performance assessment. Support solution release and deployment readiness, ensuring outputs are usable, documented, and aligned with agreed analytical standards and governance requirements. Intelligent applications promotion and continuous iteration Support the promotion and continuous improvement of intelligent applications by enabling user adoption, ensuring solution reliability, and driving iterative optimization based on business needs and feedback Support rollout and adoption of analytical and intelligent solutions by translating outputs into clear, business‑oriented language. Explain model logic, outputs, assumptions, and limitations to business users to enable correct interpretation and application in decision‑making. Provide user enablement support, including training materials, communications, and day‑to‑day guidance, to improve adoption and effective usage. Support continuous iteration and enhancement of analytical models, dashboards, and intelligent applications based on business feedback and evolving needs. Intelligent applications operations and monitoring Ensure the stable operation and ongoing performance monitoring of digital and intelligent tools by establishing operational monitoring mechanisms, identifying risks and issues early, and driving continuous improvement based on usage and performance data. Monitor day‑to‑day operation of analytical and intelligent tools, including data quality, system availability, usage patterns, and key performance indicators. Track user adoption and engagement metrics, identify under‑utilization or performance gaps, and support timely issue diagnosis and resolution. Establish regular monitoring and reporting mechanisms to assess solution effectiveness, operational risks, and value delivery. Support the stability and sustainability of analytical solutions through routine monitoring, maintenance, and issue escalation as needed. Compliance and business digital capability upskilling Enable business teams to effectively leverage digital and intelligent tools by building practical digital capabilities, strengthening data literacy, and embedding digital ways of working into day‑to‑day business operations. Work with BTS, Compliance, Legal, and Data Security teams to ensure analytical solutions follow technical, data security, and compliance guidelines. Support business teams in strengthening data literacy and practical digital capabilities, including understanding data, using analytical tools, and applying insights in daily operations. Contribute to embedding data‑driven and digital ways of working into routine business processes. Qualifications Education and Experience Bachelor’s degree or above from a top‑tier university. Educational background in Statistics, Computer Science, Data Science, or other quantitative disciplines preferred. Minimum 5 years of relevant experience in the pharmaceutical or healthcare industry; SFE‑related experience is preferred. Essential Skills, Experience, and Competencies (Includes Licenses, Credentials) Good understanding of aesthetics or pharmaceutical business and operation models Familiarity with SFE methodologies and core performance management concepts is a plus. Proficient in using BI and AI too