Description and Requirements
Job Responsibilities:
1. Data infrastructure management: Work with the team to complete the foundational setup of distributed data storage and computing architecture, and participate in the platform operation management.
2. Participate in the development of data processing procedures: Assist in the requirement analysis, script development and testing of ETL/ELT processes, and participate in basic tasks such as data collection, cleaning, and transformation to ensure data quality.
3. AI model development: Participate in the training, optimization and deployment support of AI models, learn and practice the basic applications of frameworks such as PyTorch/TensorFlow, and assist in setting up simple model validation scenarios.
4. Data Governance: Assist in organizing data classification and establishing annotation standards, participate in basic tasks such as metadata management and data quality monitoring, and contribute to the accumulation of enterprise data assets.
5. Technical Learning and Practice: Actively participate in team technical sharing and training sessions, quickly master the core technologies related to Data & AI, convert the knowledge learned at school into project practical skills, and produce basic technical documents.
6. Collaborate across teams: Assist the product and algorithm teams in completing the requirements alignment and data support tasks, participate in the analysis of business scenarios, and contribute to data-driven business optimization.
Job Requirements
1. Education and Major: Bachelor's degree or above, majoring in Computer Science and Technology, Data Science and Big Data Technology, Artificial Intelligence, Mathematics and Applied Mathematics, Statistics, etc.
2. Basic Skills: Proficient in Python programming language, familiar with SQL data query and processing those with basic knowledge of big data technologies (Hadoop, Spark) or AI frameworks (PyTorch, TensorFlow) are preferred.
3. Learning ability: Possess strong self-study skills and problem-solving abilities. Enjoy adopting new technologies and have good logical thinking and data analysis skills.
4. Project/Practice Experience: Candidates with course designs related to data processing and model training, research projects, academic competitions (such as mathematical modeling, big data competitions, AI innovation competitions), or internship experiences during their studies will be given priority.
5. Comprehensive Qualifications: Possess excellent communication and expression skills, a strong sense of teamwork and responsibility. Work is proactive, with a high level of resilience and a strong desire for personal growth.
