
[Keppel Internship Programme 2027] Intern, Data/AI Engineering (Jan - May 2027)
Keppel · Singapore
- Financial Services & Banking
- 10,001+ employees
- Singapore
Weak hiring record · median 98 days open
All reasons
- Open 97 days
- 64% of this employer's openings are over 60 days old
In its favour
- Still listed on the employer's own board
JOB DESCRIPTION
Keppel is a leading global asset manager and operator with strong capabilities in energy & environment, urban development and connectivity, creating solutions for a sustainable future.
The intern will be responsible in the following
'Build & Maintain Data Pipelines Support AI/ML Model Development & Deployment Contribute to Data Platform & AI/Agentic Solutions Learning Outcomes: 'Understand how large-scale data platforms and architectures are designed Learn best practices in data security, access control, and responsible AI usage Develop practical experience contributing to production-grade data/AI solutions in a business context Gain hands-on experience in building and managing end-to-end data pipelines Develop strong understanding of data modeling, data quality checks, and transformation techniques Become proficient in working with modern data stack tools (e.g., SQL, Python, cloud data platforms like AWS/Snowflake)
JOB REQUIREMENTS
- 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)
BUSINESS SEGMENT
Corporate
PLATFORM
Operating Division
Skills
- SQL
- Python
- AWS
- Snowflake
- ETL
- ELT
- Machine Learning
- Pandas
- NumPy
- scikit-learn
- Azure
- GCP
- BigQuery
- Databricks
- Segment
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