Educational Background & Technical Foundations Pursuing a degree in Computer Science, Data Science, Engineering, or a related field Strong fundamentals in programming (preferably Python) and SQL Basic understanding of data structures, algorithms, and software engineering principles 2. Data & AI Knowledge Familiarity with data processing concepts (ETL/ELT, data modeling, data cleaning) Exposure to machine learning fundamentals (e.g., supervised/unsupervised learning, model evaluation) Experience with common data/ML libraries (e.g., Pandas, NumPy, scikit-learn) is a plus 3. Tools & Platform Exposure Exposure to cloud platforms (e.g., AWS, Azure, or GCP) and modern data ecosystems is advantageous Familiarity with databases (SQL/NoSQL) and data warehousing concepts (e.g., Snowflake, BigQuery, Databricks) 4. Problem-Solving & Mindset Strong analytical thinking and problem-solving skills Ability to break down complex problems into structured solutions Eagerness to learn and adapt in a fast-paced, technology-driven environment 5. Communication & Collaboration Good communication skills, with the ability to explain technical concepts clearly Team player with a proactive and ownership-driven attitude Ability to work with cross-functional teams (analytics, business stakeholders)
Build global logistic and warehousing network, improve operations efficiency and reduce operational cost with data analysis, machine learning and operation research methods.
Large language Model Algorithm Development: Build domain-specific large language models (LLM/MLLM) for e-commerce, integrating domain knowledge to rapidly apply models to business scenarios.
Precision Subsidies & Intelligent Pricing: Model user demand elasticity across products and coupon values, combined with operations research optimization to solve for optimal budget allocation under strict constraints — maximizing GMV and conversion rate.
Execute the roadmap for AI applications spanning guest experience, staff productivity, and corporate process augmentation, including conversational assistants, retrieval-augmented generation systems, and purpose-built AI tools.
Provide technical leadership in the architecture, development, and deployment of production-grade machine learning solutions forgeospatial applications.