Data Scientist - Forecasting

Established in 2010, the Energy Research Institute @ NTU (ERI@N) is a pan-university research institute that focuses on systems-level research for tropical megacities. It performs translational research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which focus on translational Research, Development & Deployment which focus on specific area of the energy value chain, and a number of Living labs and Testbeds which facilitate large scale technology deployment enabling validation and demonstration of real-world applications.

For more details, please view https://www.ntu.edu.sg/erian

The primary purpose of this role is to develop and refine forecasting models that support strategic planning and operational efficiency within the energy sector. The researcher will analyze historical and real-time data to predict energy demand, supply fluctuations, and market trends, incorporating factors such as weather patterns, consumption behavior, and regulatory changes. By leveraging advanced statistical and machine learning techniques, the role aims to provide accurate, data-driven insights that inform energy production, distribution, and sustainability initiatives. The researcher will collaborate with cross-functional teams to ensure forecasts align with business goals and contribute to resilient, future-ready energy systems

Key Responsibilities:

1) Develop Forecasting Models
Design and implement statistical and machine learning models to predict energy demand, supply, and market trends.

2) Analyse Energy Data
Collect, clean, and interpret large datasets from various sources including smart meters, weather systems, and market reports.

3) Monitor and Validate Model Performance
Continuously evaluate forecasting accuracy and refine models to improve reliability and responsiveness.

4) Collaborate with Stakeholders
Work closely with energy planners, engineers, and policy teams to align forecasts with operational and strategic goals.

5) Integrate External Factors
Incorporate variables such as weather patterns, regulatory changes, and economic indicators into forecasting frameworks.

6) Support Sustainability Initiatives
Provide insights that help optimize renewable energy integration and reduce carbon footprint.

7) Communicate Insights
Present findings through reports, dashboards, and presentations to technical and non-technical audiences.

8) Stay Current with Industry Trends
Keep up-to-date with advancements in forecasting techniques, energy technologies, and market dynamics.

9) Contribute to Research Publications
Publish research findings in academic journals or industry reports to advance knowledge in energy forecasting

Job Requirements:

Core Competencies

1) Analytical Thinking
Ability to interpret complex datasets and identify patterns, trends, and anomalies.

2) Forecasting & Modelling Expertise
Proficiency in time series analysis, regression models, and machine learning techniques relevant to energy forecasting.

3) Technical Proficiency
Skilled in tools such as Python, R, MATLAB, SQL, and Excel; familiarity with energy modelling software is a plus.

4) Domain Knowledge
Understanding of energy systems, market dynamics, renewable integration, and regulatory frameworks.

5) Communication Skills
Capable of translating technical insights into actionable recommendations for diverse stakeholders.

6) Problem-Solving
Innovative and solution-oriented approach to addressing forecasting challenges and improving model accuracy.

7)Collaboration & Teamwork
Experience working in interdisciplinary teams and engaging with engineers, analysts, and policy experts.

8) Attention to Detail
Precision in data handling, model validation, and documentation.

9) Project Management
Ability to manage multiple research tasks, meet deadlines, and contribute to long-term strategic goals.

10) Continuous Learning
Commitment to staying updated with the latest forecasting techniques, energy technologies, and industry trends

Qualification

• Education:
  Master’s or PhD in Data Science, Statistics, Energy Systems, Engineering, Applied Mathematics, or a related field.

• Experience:
  3–5 years of experience in forecasting, data analysis, or energy research (postdoctoral experience preferred for senior roles).

• Technical Skills:

  • Proficiency in Python, R, or MATLAB

  • Experience with time series forecasting, machine learning, and statistical modelling

  • Familiarity with energy data sources and modelling platforms

• Certifications (Optional but Valuable):

  • Certified Energy Manager (CEM)

  • Data Science or Machine Learning certifications (e.g., from Coursera, edX, or industries bodies

We regret to inform that only shortlisted candidates will be notified.