SUMMARY OF THE ROLE
The role is mainly responsible for comprehensive external data analysis and business performance analysis for the purpose of support SMT in making key decisions, especially in national market trend reporting .
Major works include:
- Conduct national-level performance analysis and business monitoring
- Manage external data vendors and design data quality control frameworks to ensure the accuracy and reliability of market data
ROLE & RESPONSIBILITIES
1. In-depth Business Analysis and Reporting
- Monitor and analyze pharmaceutical market dynamics, competitive landscape, and business performance trends
- Integrate and analyze external market data and internal business data to generate actionable insights for business stakeholders
2. Data Quality Management
- Conduct hands-on data profiling, cleansing and anomaly identification using Power Query (Excel/Power BI) to extract, transform and load business datasets
- Ensure data consistency and data quality across all types of BI reports/AI tools and reports.
3. Leverage Generative AI, Large Language Models (LLMs) and AI Agents to Optimize Data Management & Reporting Workflows
- Design and deploy AI agent-assisted automation tools to accelerate business glossary compilation, data dictionary maintenance, critical data element extraction and cross-department KPI standard alignment for MI and behavioral reports.
REQUIREMENTS
Essential Qualifications & Experience
1. Bachelor’s degree in Information Management, Data Science, Computer Science, Pharmacy, Life Sciences, Business Administration or equivalent professional experience; Master’s degree preferred.
2. Minimum 4 years progressive hands-on experience in pharmaceutical/biotech industry, with core experience in SFE, sales & marketing business analytics; familiar with commercial data in pharmaceutical industry
3. Hands-on experience in pharmaceutical commercial performance analysis, including sales tracking, market trend analysis, and insight generation.
Preferred Qualifications
1. Strong hands-on data processing capability: Advanced proficiency in Power Query (Excel & Power BI) for data extraction, transformation, merging, deduplication, profiling and building repeatable data cleansing logic
2. Hands-on experience with LLMs and Copilot for data analysis and insight generation
Date Posted
08-10月-2026Closing Date
27-12月-2026AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
Skills Required
- Bachelor's degree in Information Management, Data Science, Computer Science, Pharmacy, Life Sciences, Business Administration or equivalent
- Master's degree
- Minimum 6 years progressive hands-on experience in pharmaceutical/biotech commercial data management, SFE data governance, sales & marketing data analysis or MI reporting
- Practical expertise in metadata management, data lineage, business glossary, critical data element definition and master data harmonization
- Advanced proficiency in Power Query (Excel & Power BI) for data extraction, transformation, deduplication, profiling and repeatable cleansing logic
- Experience deploying enterprise AI agents or generative AI tools (e.g., Copilot, Azure OpenAI) within data governance or MI reporting
- Hands-on experience building LLM-aided business glossary, data dictionary and KPI logic standardization tools
AstraZeneca Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AstraZeneca and has not been reviewed or approved by AstraZeneca.
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Fair & Transparent Compensation — Pay is considered competitive across many roles when total rewards are factored in. Senior scientific and leadership bands are described with high ranges that reinforce competitiveness at upper levels.
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Strong & Reliable Incentives — Bonuses, equity eligibility in many salaried roles, and solid sales on‑target earnings with upside are emphasized as meaningful parts of compensation. These elements boost overall value even where base pay is not the very highest.
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Retirement Support — A 401(k) program with a strong company match and immediate vesting is repeatedly cited as a standout. Generous retirement support is viewed as enhancing the total package relative to peers.
AstraZeneca Insights
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