Full Stack Data Scientist, ONC

Keppel

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Posted 22 hours ago

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.

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)