Intern - Data Engineering (Spring 2027, Jan to Jun)

Brief Overview:

As one of the key innovation drivers at Singapore Exchange, the Data & AI Engineering unit plays a pivotal role in shaping the future of data-driven solutions. We are at the forefront of emerging technologies such as Artificial Intelligence (AI), Generative AI (GenAI), Big Data, and Cloud Computing. Our responsibilities span across designing robust data and machine learning architectures, ensuring data integrity, developing data dictionaries and governance models, and building advanced analytics and visualization tools. These efforts aim to transform the organization and foster a strong data-driven culture.

Learning Outcomes

The Data & AI Engineering unit is seeking a Data Science Intern with a passion for exploring new technologies and innovative techniques. This internship offers an opportunity to challenge conventional thinking and develop creative solutions. The selected intern will gain hands-on experience with our suite of products and services, learn to build data science capabilities, and develop end-to-end production pipelines that deliver tangible business impact across multiple real-world use cases.

Job Description

• Identify business problems that can be addressed through data science and analytics.
• Propose and implement solutions to meet business needs.
• Learn to use kdb+ for extracting data from the data warehouse for modeling purposes.
• Gain experience in Google Cloud Platform (GCP) for cloud-based development.
• Build end-to-end production pipelines integrating data, models, and visualization/GUI to expose developed capabilities to business users.
• Support existing projects, platform migrations, and system improvements.

Technical responsibilities

• Data Extraction & Preparation:

• Use kdb+ and q language for querying large datasets from the data warehouse.

• Perform data cleaning and transformation using Python (pandas, NumPy etc).

• Feature Engineering & Modeling:

• Implement models using Python libraries such as scikit-learn, XGBoost, LightGBM, and TensorFlow/PyTorch for advanced ML.

• Experimentation & Optimization:

• Perform hyperparameter tuning with Optuna or scikit-learn GridSearchCV.

• Validate models using metrics libraries like scikit-learn metrics.

• Integrate visualization using matplotlib, Seaborn, Plotly, Dash, or Streamlit.

• Pipeline Development:

• Build end-to-end pipelines with Kubeflow.

• Tooling & Automation:

• Automate workflows using Python scripts, Shell scripting, and CI/CD tools.

• Cloud Development:

• Develop and deploy workflows on Google Cloud Platform (GCP) using services like BigQuery, Cloud Storage, and Vertex AI.

• Utilize Docker for containerization.

• Business Understanding & Presentation:

• Translate technical findings into actionable insights for business teams.

• Prepare clear and compelling presentations, ensuring non-technical stakeholders can easily understand the impact.

• Document workflows and results in Confluence.