Job description

We are seeking an experienced Data Scientist with a strong background in predictive analytics and machine learning, specifically within the Energy Sector. The ideal candidate will have 7+ years of experience applying advanced modeling techniques like ARIMA, LSTM, Prophet, Linear Regression, and XGBoost to solve complex problems in energy trading and risk management (ETRM) environments. The role requires expertise in Python, PySpark, and modern machine learning frameworks like TensorFlow, PyTorch, and Darts. You will work on building and deploying machine learning models for energy forecasting, risk management, and trading optimization.

Key Responsibilities

• Develop and implement machine learning models to forecast energy prices, demand, and other key market indicators, leveraging tools such as ARIMA, LSTM, Prophet, XGBoost, and Linear Regression.
• Use frameworks like TensorFlow, PyTorch, and Darts to design, train, and deploy predictive models.
• Integrate machine learning models into production environments using MLOps practices, ensuring seamless deployment and monitoring.
• Build and maintain ETRM (Energy Trading and Risk Management) solutions, specifically focusing on advanced data analysis and predictive modeling for risk and portfolio management.
• Work closely with cross-functional teams, including traders, data engineers, and IT, to understand data requirements and deliver actionable insights.
• Implement Python SDK for model development, training, and testing, while utilizing PySpark for distributed computing and large-scale data processing.
• Develop and optimize algorithms for energy forecasting, including short-term and long-term price predictions, demand forecasting, and energy production forecasts.
• Continuously improve model performance and adapt to changing market conditions, using data-driven insights to inform strategic decisions.

Required Qualifications

• 7+ years of experience as a Data Scientist, preferably within the Energy Sector or a related field such as commodities or trading.
• Expertise in ETRM systems and processes, with a strong understanding of energy markets and trading dynamics.
• Strong proficiency with machine learning techniques, including:
• ARIMA, LSTM, Prophet, Linear Regression, XGBoost.
• Deep learning frameworks like TensorFlow and PyTorch.
• Forecasting models using Darts for time series analysis.
• Experience in MLOps for deploying and maintaining machine learning models in production environments.
• Advanced knowledge of programming in Python, including Python SDK for model deployment and automation.
• Strong experience with PySpark for distributed data processing and large-scale analytics.
• Familiarity with cloud computing platforms, particularly Azure for data storage, model training, and deployment.
• Experience with data wrangling, feature engineering, and data preprocessing for large datasets.
• Excellent problem-solving skills with a passion for innovation and driving business value through data science.

Preferred Qualifications

• Experience working with ETRM platforms like Allegro, Exxeta, or Trayport.
• Background in energy trading, utilities, or commodity sectors.
• Familiarity with regulatory compliance within the energy sector.
• Strong understanding of data visualization tools like Power BI, Tableau, or Matplotlib for presenting insights.
• Experience working in an Agile development environment.