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Full Stack Data Scientist

$ 5,000 - $ 8,000 / month


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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)