ROLE & RESPONSIBILITIES

1. Advanced Analytics & Al Modeling

• Develop and implement end-to-end machine learning models to solve commercial challenges, including Marketing Mix Modeling (MMM) and Next Best Action (NBA).
• Develop high-accuracy sales forecasting models. Integrate external macro-factors and internal promotional data to enhance strategic planning and risk management.
• Explore and integrate of GenAI/LLM applications to enhance internal productivity & analytics tools
• Develop intelligent process automation (IPA) to accelerate and optimize tasks such as sales deployment, target setting, resource allocation and etc.

2. Strategic Business Partnership

• Collaborate closely with commercial stakeholders to translate "business questions" into "data science problems."
• Act as a subject-matter expert, presenting complex technical findings to non-technical leadership in a clear, "story-driven" manner.

3. Data Engineering & MLOps

• Partner with IT and global technology teams to implement solutions within cloud environments (e.g., AWS, Azure, Databricks).
• Ensure the scalability and robustness of models by following MLOps best practices, ensuring models remain accurate and high-performing in production.

4. Continuous Innovation

• Stay at the forefront of AI/ML trends.
• Proactively identify new data sources or analytical methodologies that can provide a competitive edge for AZ.

REQUIREMENTS

Education: Master’s or PhD in Data Science, Computer Science, Statistics, or a related quantitative field.

Experience: at least 5–8 years of professional experience in data science or advanced analytics.

• Proven track record of delivering end-to-end ML products (from data cleaning to production).
• Experience in Pharmaceuticals, Life Sciences, or FMCG is highly preferred.

Technical Stack:

• Expert proficiency in Python
• Deep understanding of ML frameworks
• Hands-on experience with GenAI/LLM application development (Prompt engineering, RAG, etc.).
• Familiarity with Cloud platforms (AWS/Azure) and SQL.

Soft Skills:

• Strong business acumen and the ability to "tell a story" with data.
• Project management skills with an agile mindset.

Language: Fluency in English (written and verbal).