SUMMARY OF THE ROLE
As a core member of the Advanced Analytics team, you will bridge the gap between complex data science and pharmaceutical commercial strategy. You will design and deploy advanced analytical solutions—ranging from predictive modeling to Generative AI—to optimize sales force effectiveness, enhance marketing precision, and drive data-led decision-making across AZ China’s commercial portfolio.
ROLE & RESPONSIBILITIES
1. Advanced Analytics & Al Modeling
- Design Business Driver Analytics by identifying key commercial performance indicators with leveraging causal inference or any other approaches to uncover true drivers of business impact
- Architect and deploy modular analytical frameworks that enable non-coding business users to perform self-service diagnostic deep-dives
- Develop intelligent process automation (IPA) driven solutions to streamline major commercial excellence tasks and optimize the business-ready results recommendation
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 high-performing in production.
4. Continuous Innovation
- Always 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 Science, Engineering, or a related quantitative field.
Experience: * 7–10 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 AI/ML principles and frameworks
- Practical & expertise in utilizing SOTA LLMs and coding agents
Soft Skills:
- Strong business acumen and the ability to "tell a story" with data
- Project management skills with an agile mindset
- Experienced and enthusiastic in prompt engineering
Language: Fluency in English (written and verbal).
Date Posted
04-8月-2026Closing Date
30-10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
- Master's or PhD in Science, Engineering, or related quantitative field
- 7-10 years professional experience in data science or advanced analytics
- Proven track record delivering end-to-end ML products (data cleaning to production)
- Experience in Pharmaceuticals, Life Sciences, or FMCG
- Expert proficiency in Python
- Deep understanding of AI/ML principles and frameworks
- Practical experience with SOTA LLMs and coding agents
- Experience implementing solutions in cloud environments (AWS, Azure, Databricks) and following MLOps best practices
- Experienced and enthusiastic in prompt engineering
- Fluency in English (written and verbal)
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
What We Do
We're transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet.


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