Payroll Specialist
March 7, 2025Senior Accountant
March 14, 2025Job Summary:
The Machine Learning (ML) Engineer plays a pivotal role in driving the success of the HEN trading platform. This role involves designing, developing, and fine-tuning machine learning models and algorithms tailored for practical, real-world applications. The engineer will build scalable solutions that support pricing strategies, forecast demand and supply, and track renewable energy trends that influence the market. The ideal candidate will bring deep expertise in data science, deep learning, and algorithm design to help inform trading, investment, and risk management strategies.
The Machine Learning (ML) Engineer plays a pivotal role in driving the success of the HEN trading platform. This role involves designing, developing, and fine-tuning machine learning models and algorithms tailored for practical, real-world applications. The engineer will build scalable solutions that support pricing strategies, forecast demand and supply, and track renewable energy trends that influence the market. The ideal candidate will bring deep expertise in data science, deep learning, and algorithm design to help inform trading, investment, and risk management strategies.
Responsibilities:
- Develop and refine algorithmic bidding and execution models for Day-Ahead and Real-Time markets, enhancing trading performance through advanced ML techniques.
- Perform in-depth market analytics to forecast supply, demand, congestion, and renewable generation trends, using data-driven insights to support strategic trading decisions.
- Design and implement time series forecasting models utilizing machine learning and deep learning approaches, with a focus on feature extraction and predictive accuracy.
- Monitor model performance in production, identify data drift, and manage retraining schedules to maintain effectiveness and reliability.
- Collaborate closely with traders, data scientists, and software engineers to operationalize ML-driven insights and optimize trading strategies.
Employ cloud-based infrastructure and MLOps best practices to support scalable, efficient, and automated model deployment
Qualifications:
- Education & Experience: Bachelor’s or Master’s degree with at least 2 years of relevant industry experience, or a PhD in Computer Science, Data Science, or a related discipline.
- Programming: Strong Python skills, with hands-on experience using ML libraries such as TensorFlow and Scikit-learn.
- Data Skills: Proficient in data preprocessing, feature engineering, and model evaluation techniques.
Analytical Ability: Strong problem-solving and analytical skills with the ability to derive actionable insights from complex data. - Communication: Excellent verbal and written communication skills, with the ability to clearly explain technical concepts to both technical and non-technical audiences.
- Collaboration: Proven experience working effectively in cross-functional teams.
- Adaptability: Comfortable in a fast-moving environment, with the ability to adjust to evolving market conditions.
Preferred Qualifications:
- Cloud Expertise: Familiarity with cloud platforms and tools for ML deployment.
- Optimization: Exposure to Mixed-Integer Programming is a plus.
- Energy Sector Knowledge: Understanding of power markets, industry regulations, and pricing mechanisms.
- Trading Insight: Experience in energy trading, optimization research, or revenue-focused data analysis.