ADP-ToB数据产品经理-大模型
Tencent
ADP-ToB数据产品经理-大模型
Job Description
ADP-ToB数据产品经理-大模型
📋 Job Overview
The ADP-ToB Data Product Manager for Large Models role at Tencent involves building and managing data analysis systems for large model products, focusing on key metrics like user frequency, success rates, and commercial efficiency. The position requires creating customized data frameworks for industries such as finance, manufacturing, and healthcare, while leading report development and optimization suggestions. Responsibilities also include competitive analysis, data collaboration with R&D, and supporting sales teams to enhance customer satisfaction.
📍 Location: Wuhan, China
🏢 Business Unit: CSIG
📄 Full Description
1.负责大模型产品数据分析体系搭建，涵盖用户使用频次、调用成功率、响应时长、功能渗透率、商业转化效率等核心指标，确保指标精准反映产品性能、用户价值与商业价值；
2.结合业务需求（如客户行业属性、使用场景差异），拆分细分维度数据视图，为金融、制造、医疗等不同行业客户提供定制化数据分析框架，支撑客户决策与产品优化。；
3.可以主导大模型产品数据报表规划与落地，覆盖日常运营报表、客户使用分析报表、商业收入报表等类型，可以通过 Tableau、Power BI或自研数据平台实现数据可视化呈现；
4.定期输出大模型产品数据分析报告，挖掘数据背后的业务问题（如功能使用率低、客户留存不佳等），提出功能迭代、交互改进、定价调整等产品优化建议，并推动研发、运营团队落地。；
5.跟踪行业竞品大模型数据动态，分析竞品优势与不足，结合自身产品数据制定差异化竞争策略，支撑业务增长决策。；
6.协同研发团队（算法、工程）明确数据采集范围与标准，确保大模型产品从调用请求到结果返回的全链路数据可追溯、可分析，解决数据缺失、数据不一致等问题。；
7.配合销售、客户成功团队，为客户提供数据解读服务（如客户使用数据复盘、优化建议），提升客户满意度与续约率。
2.结合业务需求（如客户行业属性、使用场景差异），拆分细分维度数据视图，为金融、制造、医疗等不同行业客户提供定制化数据分析框架，支撑客户决策与产品优化。；
3.可以主导大模型产品数据报表规划与落地，覆盖日常运营报表、客户使用分析报表、商业收入报表等类型，可以通过 Tableau、Power BI或自研数据平台实现数据可视化呈现；
4.定期输出大模型产品数据分析报告，挖掘数据背后的业务问题（如功能使用率低、客户留存不佳等），提出功能迭代、交互改进、定价调整等产品优化建议，并推动研发、运营团队落地。；
5.跟踪行业竞品大模型数据动态，分析竞品优势与不足，结合自身产品数据制定差异化竞争策略，支撑业务增长决策。；
6.协同研发团队（算法、工程）明确数据采集范围与标准，确保大模型产品从调用请求到结果返回的全链路数据可追溯、可分析，解决数据缺失、数据不一致等问题。；
7.配合销售、客户成功团队，为客户提供数据解读服务（如客户使用数据复盘、优化建议），提升客户满意度与续约率。
🎯 Key Responsibilities
- Build the data analysis system for large model products, covering core metrics such as user usage frequency, call success rate, response time, feature penetration rate, and commercial conversion efficiency, ensuring accurate reflection of product performance, user value, and commercial value.
- Combine business needs (e.g., customer industry attributes, usage scenario differences) to break down subdivided data views, providing customized data analysis frameworks for industries like finance, manufacturing, and healthcare to support customer decisions and product optimization.
- Lead the planning and implementation of large model product data reports, covering daily operational reports, customer usage analysis reports, and commercial revenue reports, using tools like Tableau, Power BI, or self-developed data platforms for visualization.
- Regularly produce large model product data analysis reports, identify underlying business issues (e.g., low feature usage, poor customer retention), propose product optimization suggestions such as feature iterations, interaction improvements, and pricing adjustments, and drive implementation with R&D and operations teams.
- Track industry competitors' large model data dynamics, analyze strengths and weaknesses, and formulate differentiated competitive strategies based on own product data to support business growth decisions.
- Collaborate with R&D teams (algorithms, engineering) to define data collection scope and standards, ensuring full-link data traceability and analysis from call requests to result returns, resolving issues like data missing or inconsistency.
- Cooperate with sales and customer success teams to provide data interpretation services (e.g., customer usage data reviews, optimization suggestions), enhancing customer satisfaction and renewal rates.
🛠️ Required Skills
- Data analysis and metrics definition
- Customized data framework development for industries
- Data reporting and visualization with Tableau, Power BI, or similar tools
- Business problem identification and product optimization
- Competitive analysis and strategy formulation
- Collaboration with R&D for data collection and standards
- Customer data interpretation and support
Locations
- Wuhan, China
Salary
Estimated Salary Rangemedium confidence
300,000 - 800,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Data analysis and metrics definitionintermediate
- Customized data framework development for industriesintermediate
- Data reporting and visualization with Tableau, Power BI, or similar toolsintermediate
- Business problem identification and product optimizationintermediate
- Competitive analysis and strategy formulationintermediate
- Collaboration with R&D for data collection and standardsintermediate
- Customer data interpretation and supportintermediate
Responsibilities
- Build the data analysis system for large model products, covering core metrics such as user usage frequency, call success rate, response time, feature penetration rate, and commercial conversion efficiency, ensuring accurate reflection of product performance, user value, and commercial value.
- Combine business needs (e.g., customer industry attributes, usage scenario differences) to break down subdivided data views, providing customized data analysis frameworks for industries like finance, manufacturing, and healthcare to support customer decisions and product optimization.
- Lead the planning and implementation of large model product data reports, covering daily operational reports, customer usage analysis reports, and commercial revenue reports, using tools like Tableau, Power BI, or self-developed data platforms for visualization.
- Regularly produce large model product data analysis reports, identify underlying business issues (e.g., low feature usage, poor customer retention), propose product optimization suggestions such as feature iterations, interaction improvements, and pricing adjustments, and drive implementation with R&D and operations teams.
- Track industry competitors' large model data dynamics, analyze strengths and weaknesses, and formulate differentiated competitive strategies based on own product data to support business growth decisions.
- Collaborate with R&D teams (algorithms, engineering) to define data collection scope and standards, ensuring full-link data traceability and analysis from call requests to result returns, resolving issues like data missing or inconsistency.
- Cooperate with sales and customer success teams to provide data interpretation services (e.g., customer usage data reviews, optimization suggestions), enhancing customer satisfaction and renewal rates.
Tags & Categories
ADP-ToB数据产品经理-大模型
Tencent
ADP-ToB数据产品经理-大模型
Job Description
ADP-ToB数据产品经理-大模型
📋 Job Overview
The ADP-ToB Data Product Manager for Large Models role at Tencent involves building and managing data analysis systems for large model products, focusing on key metrics like user frequency, success rates, and commercial efficiency. The position requires creating customized data frameworks for industries such as finance, manufacturing, and healthcare, while leading report development and optimization suggestions. Responsibilities also include competitive analysis, data collaboration with R&D, and supporting sales teams to enhance customer satisfaction.
📍 Location: Wuhan, China
🏢 Business Unit: CSIG
📄 Full Description
1.负责大模型产品数据分析体系搭建，涵盖用户使用频次、调用成功率、响应时长、功能渗透率、商业转化效率等核心指标，确保指标精准反映产品性能、用户价值与商业价值；
2.结合业务需求（如客户行业属性、使用场景差异），拆分细分维度数据视图，为金融、制造、医疗等不同行业客户提供定制化数据分析框架，支撑客户决策与产品优化。；
3.可以主导大模型产品数据报表规划与落地，覆盖日常运营报表、客户使用分析报表、商业收入报表等类型，可以通过 Tableau、Power BI或自研数据平台实现数据可视化呈现；
4.定期输出大模型产品数据分析报告，挖掘数据背后的业务问题（如功能使用率低、客户留存不佳等），提出功能迭代、交互改进、定价调整等产品优化建议，并推动研发、运营团队落地。；
5.跟踪行业竞品大模型数据动态，分析竞品优势与不足，结合自身产品数据制定差异化竞争策略，支撑业务增长决策。；
6.协同研发团队（算法、工程）明确数据采集范围与标准，确保大模型产品从调用请求到结果返回的全链路数据可追溯、可分析，解决数据缺失、数据不一致等问题。；
7.配合销售、客户成功团队，为客户提供数据解读服务（如客户使用数据复盘、优化建议），提升客户满意度与续约率。
2.结合业务需求（如客户行业属性、使用场景差异），拆分细分维度数据视图，为金融、制造、医疗等不同行业客户提供定制化数据分析框架，支撑客户决策与产品优化。；
3.可以主导大模型产品数据报表规划与落地，覆盖日常运营报表、客户使用分析报表、商业收入报表等类型，可以通过 Tableau、Power BI或自研数据平台实现数据可视化呈现；
4.定期输出大模型产品数据分析报告，挖掘数据背后的业务问题（如功能使用率低、客户留存不佳等），提出功能迭代、交互改进、定价调整等产品优化建议，并推动研发、运营团队落地。；
5.跟踪行业竞品大模型数据动态，分析竞品优势与不足，结合自身产品数据制定差异化竞争策略，支撑业务增长决策。；
6.协同研发团队（算法、工程）明确数据采集范围与标准，确保大模型产品从调用请求到结果返回的全链路数据可追溯、可分析，解决数据缺失、数据不一致等问题。；
7.配合销售、客户成功团队，为客户提供数据解读服务（如客户使用数据复盘、优化建议），提升客户满意度与续约率。
🎯 Key Responsibilities
- Build the data analysis system for large model products, covering core metrics such as user usage frequency, call success rate, response time, feature penetration rate, and commercial conversion efficiency, ensuring accurate reflection of product performance, user value, and commercial value.
- Combine business needs (e.g., customer industry attributes, usage scenario differences) to break down subdivided data views, providing customized data analysis frameworks for industries like finance, manufacturing, and healthcare to support customer decisions and product optimization.
- Lead the planning and implementation of large model product data reports, covering daily operational reports, customer usage analysis reports, and commercial revenue reports, using tools like Tableau, Power BI, or self-developed data platforms for visualization.
- Regularly produce large model product data analysis reports, identify underlying business issues (e.g., low feature usage, poor customer retention), propose product optimization suggestions such as feature iterations, interaction improvements, and pricing adjustments, and drive implementation with R&D and operations teams.
- Track industry competitors' large model data dynamics, analyze strengths and weaknesses, and formulate differentiated competitive strategies based on own product data to support business growth decisions.
- Collaborate with R&D teams (algorithms, engineering) to define data collection scope and standards, ensuring full-link data traceability and analysis from call requests to result returns, resolving issues like data missing or inconsistency.
- Cooperate with sales and customer success teams to provide data interpretation services (e.g., customer usage data reviews, optimization suggestions), enhancing customer satisfaction and renewal rates.
🛠️ Required Skills
- Data analysis and metrics definition
- Customized data framework development for industries
- Data reporting and visualization with Tableau, Power BI, or similar tools
- Business problem identification and product optimization
- Competitive analysis and strategy formulation
- Collaboration with R&D for data collection and standards
- Customer data interpretation and support
Locations
- Wuhan, China
Salary
Estimated Salary Rangemedium confidence
300,000 - 800,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Data analysis and metrics definitionintermediate
- Customized data framework development for industriesintermediate
- Data reporting and visualization with Tableau, Power BI, or similar toolsintermediate
- Business problem identification and product optimizationintermediate
- Competitive analysis and strategy formulationintermediate
- Collaboration with R&D for data collection and standardsintermediate
- Customer data interpretation and supportintermediate
Responsibilities
- Build the data analysis system for large model products, covering core metrics such as user usage frequency, call success rate, response time, feature penetration rate, and commercial conversion efficiency, ensuring accurate reflection of product performance, user value, and commercial value.
- Combine business needs (e.g., customer industry attributes, usage scenario differences) to break down subdivided data views, providing customized data analysis frameworks for industries like finance, manufacturing, and healthcare to support customer decisions and product optimization.
- Lead the planning and implementation of large model product data reports, covering daily operational reports, customer usage analysis reports, and commercial revenue reports, using tools like Tableau, Power BI, or self-developed data platforms for visualization.
- Regularly produce large model product data analysis reports, identify underlying business issues (e.g., low feature usage, poor customer retention), propose product optimization suggestions such as feature iterations, interaction improvements, and pricing adjustments, and drive implementation with R&D and operations teams.
- Track industry competitors' large model data dynamics, analyze strengths and weaknesses, and formulate differentiated competitive strategies based on own product data to support business growth decisions.
- Collaborate with R&D teams (algorithms, engineering) to define data collection scope and standards, ensuring full-link data traceability and analysis from call requests to result returns, resolving issues like data missing or inconsistency.
- Cooperate with sales and customer success teams to provide data interpretation services (e.g., customer usage data reviews, optimization suggestions), enhancing customer satisfaction and renewal rates.
Tags & Categories