China jobs in IT - Big Data

一家外资零售快消行业巨头的ETL开发岗位，提供你在快节奏环境中与年轻的团队一起成长学习的机会，帮助完成在大陆地区核心项目的数据库的搭建。

• Develop and maintain data models that align with business requirements and technical constraints.
• Collaborate with cross-functional teams to define data architecture strategies.
• Ensure data integrity, security, and compliance with industry standards.
• Lead data integration projects and manage data migration activities.

• You will work on various aspects of Big data analytics and development, Data Management, Governance and Information Architecture.
• Design and develop data solution architecture on scalability, good performance and flexibility throughout the entire data life cycle (ingestion, storage and consumption
• Define and prioritise product requirements, user stories, and acceptance criteria for data and analytics products and ensure what was agreed gets delivered

1. Led data science initiatives across multiple business units (Buying, Finance, Clienteling, Digital) to support commercial decision-making through predictive modeling and customer analytics

2. Projects included traffic forecasting, sales prediction and pricing simulation, customer segmentation, and model deployment using MLOps tools in AWS

负责公司的业务数据BP，与各业务和技术部门密切合作，推动数据建模、数据开发、数据质量的提升，协助制定数据驱动的业务策略。

1、数据分析 - 结合业务痛点通过数据分析找到生意现象的本质原因，及推荐决策和期待的决策效果；

2、发展战略制定 -结合业务发展目标和现今痛点，定位并量化业务未来的发展主要动力，能制定未来短期中期和长期业务发展规划和战略，用数据指导业务发展战略；

工作职责：

负责大数据分析咨询业务的售前客户需求沟通和理解，熟悉企业经营方式，深刻理解企业营销、销售、产研、财务、售后等至少两个以上领域，基于公司的大数据/AI产品和服务为客户定制解决方案

This role provides you with the opportunity to grow together with a young team in a good environment, and assist in completing the AI project landing for various BU groups in the Asia-Pacific region.

Job Responsibilities:

• Data Analysis: Perform in-depth data analysis to identify trends and insights.
• Product Development: Drive the development of data-driven products and services.
• User Experience: Ensure data products meet the needs of end-users and enhance their experience.

工作职责：
1. 主要职责： 构建金融支付核心产品的关键业务指标体系，监控数据异常，分析潜在问题，及时识别业务风险，并提供专业建议以支持决策。
2. 数据驱动决策： 与业务团队合作，进行数据建模和迭代优化，运用AB测试等方法评估改进效果，推动业务部门的数据驱动决策。