阿斯利康
Director, Agentic AI Platform, China
Director, Agentic AI Platform, China
发布于 4 小时前中层管理(经理/总监)
全职员工仅现场办公产品经理
产品经理
Agentic Ai
Ai制药
产品管理
利益相关者管理
跨部门协作
路线图规划
需求工程
值不值得投
75综合评分
综合评分 75
前沿AI制药平台负责人，侧重技术突破与职业发展，现场办公为主，使命驱动型岗位
从薪资福利、成长发展、工作生活和使命价值四个维度综合评估，方便横向比较。
薪资怎么样
8.0万
比产品经理中位数高
发布情况
新岗位 · 今天发布
公司情况
这家公司招聘很活跃
约 250 个在招
市场机会
选择面较广
上海 · 产品经理 · 约 510 个在招
职位详情
关于这个职位
负责阿斯利康中国研发部门的Agentic AI平台产品战略与落地
连接内部需求与交付，制定产品愿景与路线图，推动从概念到生产的全生命周期管理
需跨部门协调研发、商务及全球AI团队，确保平台符合企业AI战略并解决复杂的药物发现业务问题
最低要求
Experience:
+ years in science-based technical product management, or equivalent experience in AI/ML development
Track record of developing strategic product ideas and plans, building technical proposals, and delivering product visions
Demonstrated ability to drive an AI-based solution from idea to production
Experience working in cross-functional or matrix environments, influencing across stakeholders
Skills:
Good understanding of ML/AI/DL techniques, preferably with hands-on experience
Ability to manage trade-off decisions between business opportunities and available resources
Strong requirements engineering following lean and agile principles, with experience in scrum
Highly collaborative, with strong stakeholder communication and management across technical and non-technical audiences
Able to translate across communication gaps around highly technical information and lead diverse teams toward a common goal
Understanding of R&D, data science and AI, informatics, records management, and system development in the biotech/pharma industry
China-Specific:
Ability to work in Shanghai or Beijing on-site
Mandarin fluency required
Experience with data and AI regulations in China (e.g., PIPL, DSL, and cross-border data transfer requirements) is highly desirable
Familiarity with the Beijing talent and innovation ecosystem is an advantage
Mindset:
Comfortable with ambiguity: the center and the platform are still being built and priorities will evolve
Bias to ownership: takes end-to-end accountability for the product and drives it without prompting
Passion for learning new technologies and applying them to real scientific problems
Energized by leading and influencing across functions to deliver shared outcomes
工作职责
Product Vision & Roadmap (35%)
Connect with internal customers across China R&D to identify, frame, and prioritize the problems the Agentic AI platform will solve
Create a compelling product vision and strategy for the Agentic AI platform that solves complex R&D business problems and align to China R&D and enterprise priorities
Own the product roadmap and blueprint — sequencing capabilities to ensure value delivery through well-scoped MVPs and prioritized use cases
Manage trade-off decisions between business opportunities and available resources, using data and evidence to drive prioritization
Assess build, buy, and partner options for platform capabilities, evaluating partnership and technology opportunities for strategic fit and feasibility
Execution & Delivery (40%)
Own the entire product lifecycle of the Agentic AI platform, working with engineers, scientists, and other stakeholders to deliver the right products
Drive execution, implementation, and delivery of prioritized use cases from idea to production, following lean and agile principles
Translate requirements into delivery through clear requirements engineering, backlog ownership, and scrum-based ways of working
Measure and communicate impact — define success metrics for the platform and its use cases, instrument them, and use the results to refine the roadmap
Explore and solicit innovative concepts and technologies with stakeholders, incorporating emerging agentic AI capabilities into the platform
Stakeholder Engagement & Communication (25%)
Interface across China R&D, BD, and global functions including Enterprise AI — ensuring the China platform aligns with and contributes to AstraZeneca’s enterprise AI strategy
Independently represent the platform and the team, communicating comfortably across organizations and at varying technical levels
Escalate when necessary to unblock delivery and reconcile business strategy with technical needs, without damaging relationships
Evangelize the platform’s capabilities and accomplishments through targeted communications, celebration of achievements, and internal and external forums
优先资格
Nice-to-Have:
Experience applying AI technologies to scientific or consumer products from idea to production
Experience with AI data preparation techniques in bioinformatics, ML frameworks, and tooling
Knowledge of (immune) oncology and drug development processes
Clinical, diagnostic, or translational science operations experience
Experience driving innovation events (e.g., hackathons) toward novel solutions
AI 洞察
优缺点分析
优点
- 聚焦前沿Agentic AI与AI制药交叉领域，技术栈领先
- 跨国药企平台提供稳定资源与全球化视野
- 直接赋能核心研发管线，业务影响力大
- 平台处于早期建设阶段，需求模糊且优先级频繁调整
- 需同时平衡科研严谨性与互联网敏捷交付节奏
- 跨地域/跨层级沟通协调成本高
- 适合具备深厚技术产品背景、热爱生命科学交叉领域、能在高不确定性环境中推动落地的复合型管理者
缺点 / 挑战
暂无明显挑战项
角色解读
- 可向集团级AI产品副总裁或首席产品官发展
- 也可横向拓展至AI制药整体业务线负责人
- 长期可转型为医疗科技领域的战略咨询或创业合伙人
- 负责中国研发线Agentic AI平台的整体产品战略、路线图规划与商业化落地
- 主导跨部门协作，将复杂药物发现需求转化为可执行的AI产品方案
- 监控平台使用数据与业务影响，持续迭代优化
- 深厚的AI/ML技术理解与产品化经验
- 精通敏捷开发与需求工程，能高效管理Backlog与Scrum流程
- 卓越的跨文化/跨职能沟通与利益相关者管理能力
- 熟悉生物医药研发流程及中国数据合规政策
申请策略
- 提前研究阿斯利康近期AI合作动态与管线进展
- 面试时准备具体案例说明如何在模糊需求中定义MVP并获取干系人共识
- 突出AI/ML产品从0到1的完整落地案例
- 量化展示跨部门协作效率提升或业务指标改善
- 强调医药/生物信息学领域经验及数据合规知识
- 深化对Agent架构、RAG及大模型微调技术的理解
- 补充临床试验数据管理或GxP合规相关知识
- 提升财务与商业分析能力以支撑ROI决策
面试指南
- 采用STAR法则结构化回答
- 重点突出“需求洞察-技术选型-敏捷迭代-数据验证”闭环
- 强调以业务价值为导向的技术取舍逻辑
- 请分享一个你从0到1搭建AI产品的完整案例，如何定义MVP？
- 当研发团队与工程团队在技术实现上产生分歧时，你如何决策？
- 如何评估Agentic AI平台在药物发现中的实际业务价值？
- 面对中国严格的跨境数据法规，你会如何设计平台的数据架构？
- 复习主流LLM/Agent框架特性及AI制药应用场景
职位点评
薪资福利75
成长发展85
工作生活60
使命价值80
薪资福利
75中等
总监级岗位提供具有竞争力的薪酬包，跨国药企福利体系完善，但JD未明确具体薪资范围。
薪资信号面议 (6.5千–9.5万/月)
成长发展
85较高
处于AI制药前沿赛道，平台从零搭建提供极大成长空间，技术与管理双通道发展明确。
技术前沿前沿/新兴技术
技术栈Agentic AI、LLM、Machine Learning、Deep Learning、Scrum
业务类型cost_center
工作生活
60中等
明确要求现场办公，工作强度未明确说明，但总监级岗位通常伴随较高责任压力与跨时区协作需求。
工作模式仅现场办公
办公地点科技园/产业园
加班情况未提及（无法判断）
使命价值
80较高
致力于通过AI加速新药研发，直接关联人类健康与生命科学研究，社会价值与使命感强烈。
行业发展高速增长赛道
社会影响正向社会影响力较高
使命信号accelerate drug discovery through AI、apply foundation models, agentic AI, and large-scale scientific computing to real R&D problems
创新程度积极采用新技术
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