实习岗位详情

L'Oreal - R&I - Data&AI Intern
 招聘时间
即日起 至 2027-01-01
 招聘单位
欧莱雅
 所在地区
上海
 工作地点
上海市/上海市/浦东新区 欧莱雅中国研发和创新中心 金豫路550号
 学历要求
本科
 实习时长
每周4天,每天6小时
 职责描述
Key Requirements (Must-Have):
• Passion for Data & AI: A strong, genuine interest and passion for Data and AI, with a clear aspiration to pursue a long-term career path in this domain.
• Technical Core Skills:
o Proven ability to design and build interactive Power BI dashboards.
o Hands-on basic experience with programming languages such as Python and R.
o Solid foundation in Power Query and DAX for data transformation and modeling.
o Basic understanding and experience with Data Warehousing (数仓) concepts and architectures.
• Prior Experience: Must have at least one or more previous internship experience.

Preferred Qualifications (Nice-to-Have / Better to Have):
• AI Project Experience: Prior experience working on AI-related projects, with a strong preference for applications in Consumer Insights.
• Advanced AI & Data Techniques: Hands-on experience or familiarity with:
o Knowledge Graphs
o Prompt Engineering
o Fine-tuning of LLMs

Position Requirements: Key Requirements (Must-Have):
• Passion for Data & AI: A strong, genuine interest and passion for Data and AI, with a clear aspiration to pursue a long-term career path in this domain.
• Technical Core Skills:
o Proven ability to design and build interactive Power BI dashboards.
o Hands-on basic experience with programming languages such as Python and R.
o Solid foundation in Power Query and DAX for data transformation and modeling.
o Basic understanding and experience with Data Warehousing (数仓) concepts and architectures.
• Prior Experience: Must have at least one or more previous internship experience.

Preferred Qualifications (Nice-to-Have / Better to Have):
• AI Project Experience: Prior experience working on AI-related projects, with a strong preference for applications in Consumer Insights.
• Advanced AI & Data Techniques: Hands-on experience or familiarity with:
o Knowledge Graphs
o Prompt Engineering
o Fine-tuning of LLMs
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