岗位职责：
数据分析与建模：参与公司数据分析项目，包括数据清洗、数据分析洞察及数据建模。
AI应用研究：学习并探索AI在业务中的应用，例如机器学习、自然语言处理、计算机视觉等，支持智能化产品开发与优化。
数据可视化与报告：使用Power BI、Tableau或Python等工具，制作数据可视化报表，提供数据洞察支持业务决策。
数据管道与管理：协助构建和优化数据仓库、数据湖及ETL流程，提高数据质量和可用性。
跨部门合作：与业务团队、IT团队紧密合作，理解业务需求并提供数据驱动的解决方案。
技术学习与创新：跟踪AI和数据分析领域的前沿技术，参与内部技术分享，不断提升自身能力。
任职要求：
教育背景：
计算机科学、数据科学、人工智能、统计学、数学、工程等相关专业本科及以上学历。
在校期间有AI、数据分析相关项目经验者优先。
技术能力：
具备扎实的数据分析基础，熟悉Python（Pandas、Numpy、Scikit-learn等）或R等数据分析工具。
理解基本的机器学习概念，有实际操作经验（如分类、回归、聚类等）。
熟悉SQL，能进行数据查询和简单的数据管理。
了解Power BI、Tableau，Quick BI等数据可视化工具者优先。
熟悉云计算平台（AWS、Azure、ALI）上的AI工具和数据处理流程。
具备NLP、计算机视觉、强化学习等方向的深入研究经历。
学习能力与思维方式：
具备较强的逻辑思维和问题解决能力，能快速学习新知识并应用到实际工作中。
对AI和数据分析充满好奇心，愿意深入研究和探索前沿技术。
沟通与团队合作：
具备良好的沟通能力和团队协作精神，能够清晰表达技术概念并与业务团队有效对接。
有较强的抗压能力，能在多任务环境下高效工作。
Job Responsibilities:
Data Analysis & Modeling: Participate in company data analysis projects, including data cleaning, data insights and data modeling.
AI Application Research: Learn and explore AI applications in business scenarios, such as machine learning, natural language processing (NLP), and computer vision, to support intelligent product development and optimization.
Data Visualization & Reporting: Utilize tools such as Power BI, Tableau, Quick BI or Python to create data visualization reports and provide insights for business decision-making.
Data Pipeline & Management: Assist in building and optimizing data warehouses, data lakes, and ETL processes to improve data quality and usability.
Cross-Department Collaboration: Work closely with business and IT teams to understand business requirements and provide data-driven solutions.
Technical Learning & Innovation: Stay updated with the latest advancements in AI and data analytics, participate in internal knowledge-sharing sessions, and continuously improve skills.
Qualification:
1. Educational Background:
Bachelor’s degree or above in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or related fields.
AI and data analytics project experience during university studies is preferred.
2. Technical Skills:
Solid foundation in data analysis, proficient in Python (Pandas, NumPy, Scikit-learn) or R.
Understanding of fundamental machine learning concepts with hands-on experience in classification, regression, and clustering.
Proficient in SQL for data querying and basic data management.
Familiarity with data visualization tools such as Power BI, Tableau, or Quick BI is preferred.
Experience with AI tools and data processing on cloud platforms (AWS, Azure, Ali).
In-depth research experience in NLP, computer vision, or reinforcement learning is a plus.
3. Learning Ability & Problem-Solving Skills:
Strong analytical and problem-solving skills, with the ability to quickly learn new technologies and apply them effectively.
Passion for AI and data analytics, with a strong curiosity and willingness to explore cutting-edge technologies.
4. Communication & Teamwork:
Strong communication and teamwork skills, with the ability to clearly articulate technical concepts and collaborate effectively with business teams.
Ability to work under pressure and manage multiple tasks efficiently.