Data & AI Analytics Intern: Build Scalable Insights

Tencent

Singapore

On-site

SGD 13,000 - 22,000

Full time

14 days+

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Job summary

Tencent in Singapore invites a data science internship within the WeChat operations team to analyze large-scale data, identify opportunities, and build scalable data pipelines across product, engineering and security. The internship is paid, full-time, and lasts a minimum of 12 weeks.

You will be mentored by senior team members, develop dashboards and insights, collaborate with product, operations and legal teams, and apply AI-assisted analytics to drive practical improvements for billions of

Qualifications

Pursuing Bachelor's or Master's in Data Science, CS, Statistics, Mathematics, or related field.
Experience in data analytics and data engineering, including building data pipelines, statistical analysis, and data visualization.
Proficiency in SQL and Python, with experience working with large-scale data platforms (e.g., Hive, Spark, or similar).
Experience building data dashboards and reporting tools (Tableau, Power BI, Superset, or similar).
Familiarity with AI-assisted data analysis tools, including prompt engineering, context engineering, and AI workflow integration.
Experience with RAG (Retrieval-Augmented Generation), AI agents, MCP frameworks, or similar AI infrastructure is a strong plus.
Strong problem-solving skills with the ability to break down complex problems into structured analytical approaches.
Excellent learning ability, curiosity for new technologies, and a strong sense of ownership and self-driven execution.

Responsibilities

Analyze large-scale datasets to identify business opportunities, detect patterns, and optimize product performance across the platform.
Develop data dashboards and visualization tools to track key performance metrics and provide actionable product insights.
Design and build scalable data pipelines to support analytics, operational decision-making, and platform security initiatives.
Collaborate with cross-functional stakeholders (product, engineering, operations, and security teams) to translate analytical findings into business strategies and product improvements.
Leverage AI-powered tools and technologies (e.g., LLMs, AI copilots, data assistants) to enhance analysis efficiency, automate workflows, and improve decision accuracy while maintaining robust traditional data engineering and analytics practices.
Explore and integrate AI-driven methodologies to optimize analytical processes, including automated data exploration, report generation, and intelligent insight discovery.