Fundemental Analyst

As a Quantitative Analyst, you will become part of Eneco’s Intraday Trading Team. The team trades various European intraday power markets. In this role, you will model fundamental market data for systematic trading execution. You will spend your time exploring large datasets, ranging from granular weather forecasts and renewable generation data to real-time order book updates, to uncover patterns that predict price movements and grid imbalances. Finding the pattern is only half the job; you are responsible for transforming those insights into usable tools for the traders. This means you will be developing live signals, robust price forecast models, and intuitive dashboards. 

This is a hands-on role where your code hits production. You will be building decision-support tools that are used live on the desk, constantly iterating on your models based on immediate market feedback. You work in a unified team where traders and analysts work closely together. While you will pick up specific questions from the desk, you are also expected to be creative and proactive, initiating your own research to show the team where the value lies and contributing directly to the team. 

• You have a quantitative MSc or PhD in Econometrics, Mathematics, Statistics, Computer Science, Physics, or a related field;

• You bring 5+ years of experience in a quantitative role. Experience in Intraday Power markets or other energy commodities is preferred;

• Strong Python proficiency is a must: You are an expert in data manipulation (Pandas, NumPy) and comfortable with statistical modeling libraries (Scikit-learn, Statsmodels); 

• You have a solid grounding in statistics and time-series analysis;

• You are familiar with - parts of - our tech stack: Azure Cloud, Kubernetes, Databricks, Airflow. You do not hesitate to reach out to stakeholders from the business or IT side to make sure you build the best solution possible;

• You have a pragmatic, result-oriented mindset: you understand that a perfect model is useless if it’s too slow to run in a real-time market. 

• Predicting Intraday power prices and system imbalances distributions. Quantifying the impact of fundamental drivers (wind/solar forecast errors, plant outages) on the market;

• Taking full ownership of the full model lifecycle. From initial research and development to deployment and day-to-day maintenance;

• Backtesting and validating trading ideas to assess their risk and profitability before they go live;

• Translating your quantitative signals into automated strategies;

• Conducting post-trade analysis to quantify execution performance and identify areas for improvement.