Assistant Data Intelligence Application Manager

Job Responsibilities： 1.Drive the design and implementation of AI-driven data application solutions: Identify and design AI application scenarios (AI data querying, intelligent report generation, predictive models, automated analysis agents, etc.) based on business contexts. Lead the solution design, effectiveness validation, and scaled rollout of AI-driven data solutions, transforming data assets into actionable business insights. 2.Promote "AI Ready" data asset construction and data governance: Develop data ingestion standards and AI adaptation specifications; establish data asset catalog and quality monitoring systems. Design and implement data governance frameworks (metadata, data standards, data security, etc.), drive cross-departmental data specification implementation, and continuously improve data availability and security to meet the requirements of large language model applications. 3.Drive the implementation of AI Agent business applications: Focus on management decision-making and frontline operational scenarios, explore business needs, lead the design, development, and promotion of AI agents (intelligent data querying agents, report generation agents, decision-assistance agents, etc.), and realize data value monetization. 4.Manage the end-to-end lifecycle of AI data application projects: Coordinate resource alignment, progress control, and risk management of data application projects to ensure high-quality project delivery and business goal achievement. Drive the closed-loop implementation of projects from PoC to scaled production. 5.Support the team lead in formulating AI technology roadmaps and team planning, and efficiently execute special tasks assigned by superiors: Track the evolution of mainstream large language models and AI Agent technologies, accumulate "AI + Data" internal practice cases, and form reusable methodologies and templates. 6. Core Competency Requirements (must be proficient in at least one): 7.Proficient in AI-driven data application solution design: Deep understanding of the capability boundaries and application scenarios of LLMs and AI Agents, with hands-on experience in leading or deeply participating in AI Agent development; familiar with the application of RAG, Prompt Engineering, and Function Calling in business scenarios; able to abstract complex business problems into AI-implementable solutions. 8.Proficient in AI-oriented data modeling and metrics system construction: Deep understanding of "Data for AI" logic; familiar with the full lifecycle of data governance, data standardization, and metrics system construction; able to abstract complex data warehouse table structures into business metrics, dimensions, and calculation logic; capable of designing semantic layers to lower the LLM comprehension threshold. 9.Proficient in data application and business enablement: Skilled at translating data capabilities into business solutions, with implementation experience in management dashboards, intelligent recommendation, risk warning, user profiling, etc.; able to identify AI enablement opportunities from a business perspective. Basic Requirements: Education: Bachelor degree or above in Computer Science, Data Science, Information Technology, Statistics, Artificial Intelligence, Management Science, or related fields. Work Experience: 1.For Senior Analyst: 5+ years of experience in data analysis, data products, data governance, or enterprise-level project delivery, including 2+ years of AI/LLM-related project experience. For Assistant Manager: 8+ years of relevant work experience, including 2+ years of AI/LLM-related project experience. 2.Hands-on experience in AI Agent development with proven business outcomes is preferred. 3.Technical Skills: Proficient in SQL and Python; experienced in visualization tools such as FineBI, Power BI, or Tableau; familiar with data warehouse modeling (Kimball/Inmon) and ETL development processes; understanding of mainstream LLM capabilities (GPT, Claude, DeepSeek, Qwen); hands-on experience with AI tools such as Dify, Coze, or LangChain is preferred. 4.Plus Points: Experience in LLM application and designing AI+Data solutions based on business scenarios; familiarity with vector databases (Milvus, Qdrant) and RAG architecture is preferred. Business Insight: Keen business insight, able to quickly identify business pain points and design data-driven solutions. 5.Documentation Skills: Excellent document writing ability, able to independently produce high-quality product requirement documents (PRD) and technical solutions. 6.Cross-functional Communication: Excellent cross-departmental communication and coordination skills, able to effectively drive collaboration among technology, business, and operations teams. 7.Project Management: Excellent project management skills, familiar with agile development processes, capable of risk prediction and resource integration. 8.Language: Good oral and written skills in both Chinese and English.