We are seeking an experienced data scientist/machine learning engineer to develop a predictive machine learning model using smart meter data. The goal is to analyze household electricity consumption patterns to extract meaningful insights for utilities and energy consultants. These insights will focus on identifying households with high energy-saving potential, predicting willingness to invest in value-added services (VAS) like solar PV systems and heat pumps, and providing customer segmentation based on energy usage behaviors.

The freelancer will be responsible for:

Feature extraction from smart meter data (e.g., consumption figures, statistical properties)
Implementing and comparing machine learning classifiers (e.g., kNN, SVM, Random Forests) to predict household properties such as occupancy, appliance stock, and energy-saving potential
Optimizing models for accuracy and scalability
Providing actionable insights that utilities can use to target households for energy consulting services
Requirements:

Experience with machine learning techniques and energy consumption data
Proficiency in Python or R, and relevant machine learning libraries (e.g., scikit-learn, TensorFlow)
Familiarity with feature engineering and classifiers like kNN, SVM, and Random Forests
Ability to deliver clean, well-documented code and provide detailed analysis reports
This project aims to improve energy efficiency and customer engagement for utility providers by leveraging advanced analytics and machine learning.