JOB DESCRIPTION
This role encompasses two primary responsibilities, the first involves data science activities and the second focus on full-stack development.
Data Science Responsibilities:
- Collect, clean, and preprocess large datasets
- Develop and deploy machine learning models to address challenges related to Keppel energy systems, including predictive modeling, classification, and clustering
- Apply statistical analysis to derive actionable insights and support data-driven decision-making
- Perform data visualization to effectively communicate findings to both technical and non-technical stakeholders
- Collaborate with data science and engineering teams to identify opportunities for leveraging data in product development and operations
Full Stack Development Responsibilities:
- Design, develop, and maintain scalable web applications, ensuring seamless integration with data pipelines and models
- Build APIs and microservices to serve machine learning models and integrate with front-end interfaces
- Implement responsive front-end interfaces using modern frameworks (e.g., React) to display data insights and support interactive user experiences
- Optimize back-end services for efficiency and scalability, ensuring secure data access and real-time performance
- Troubleshoot and debug issues across the full stack (front-end, back-end, and database layers)
Such other duties and responsibilities as may be reasonably requested by your immediate supervisor / the head of department from time to time.
JOB REQUIREMENTS
- Bachelor's degree in computer science, software engineering or related field
- Strong knowledge of machine learning algorithms, statistical analysis, and data mining techniques.
- Strong understanding of back-end frameworks and technologies (e.g., Node.js, Django, Flask, Express).
- Proficiency in programming languages such as Python for data science applications.
- Proficiency in front-end technologies
- Hands-on experience with databases and cloud-based data storage solutions.
- Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud)
