Data Science Internship at Spectral
1AN, , Netherlands -
Full Time


Start Date

Immediate

Expiry Date

31 Oct, 25

Salary

0.0

Posted On

03 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

ABOUT US

Spectral is a smart energy systems and platforms integrator driven by purpose. Presently, the grid’s capacity for renewable energy is limited (10-20%) because of the fluctuating supply from renewable sources, to maintain stability. A successful clean energy transition hinges on innovative approaches to energy management and new business models. Spectral develops technological solutions for energy storage, demand flexibility, and local renewable energy to maximize their potential and build better smart grids.
As Spectralites, we are dedicated to accelerating the world’s transition to a sustainable energy future. Achieving such a large-scale system change will require a monumental effort from everyone, and it will undoubtedly present significant hurdles. Rapid electrification across sectors and renewable energy expansion are creating grid congestion and complex balancing challenges. Our team is addressing the fascinating but difficult issue of energy inefficiency in modern structures, even with improved standards, focusing on poor climate system management and insufficient data transparency. By developing and deploying advanced technology platforms, we’re driving faster change in real estate and energy, resulting in well-connected, more sustainable, and high-value assets.

Responsibilities
  • Develop and experiment with multiple time series forecasting models to predict building energy demand patterns
  • Analyze smart meter data and building performance metrics to identify key features and patterns that influence energy consumption
  • Implement and compare various forecasting approaches (ARIMA, XGBoost, Prophet, etc.) to determine the optimal modeling approach
  • Conduct data preprocessing, feature engineering, and model validation to ensure robust and accurate predictions
  • Create visualizations and reports to communicate model performance, insights, and recommendations to stakeholders
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