Data Scientist Short Term Trading

Overview

The world around us is changing fast. Eneco is a frontrunner in the energy transition by integrating new sustainable energy sources and innovative ways of storing and managing energy. At Eneco Trading, our international trading division, we aim to be a leading player in the European energy market, delivering flexibility and grid stability through sustainable energy. At the core of this mission lies our commitment to using advanced data science to operate and optimize our complex and diverse asset portfolio.

We are looking for someone to help shape a sustainable future.

As a Data Scientist in the Flex Optimization team, you’ll work on data driven solutions that maximize the value of Eneco’s steerable asset portfolio by optimizing their flexibility across short-term power and ancillary markets.

Your work will impact Eneco’s trade result and contribute to a smarter, cleaner energy system. You will build optimization strategies, bidding algorithms and machine learning models that operate in fast-moving, real-time energy markets (such as real time trading applications for battery optimization). You will work side by side with other data scientists and traders to identify where data science can add the most value.

We emphasize a fast feedback loop of deploying, learning and adjusting, with a strong focus on ownership and scalable automation of processes. Deploying your model makes real-world impact—often the same day.

You will work with large and growing datasets, so continual improvement of forecasting, modelling, and decision support tooling is essential to stay ahead. Besides developing these models, you will play a pivotal role in product ionizing them in a robust and reliable manner.

• Developing and maintaining advanced analytical models for cross-market optimization and bidding strategies. Using machine learning and (non) linear optimization methodologies.
• Driving automation and scalability, identifying where manual processes can be streamlined and operationalized.
• Identifying new ways to add value with data science in close collaboration with the traders to deliver integrated, high-impact solutions.

• You have a quantitative MSc or equivalent experience in Computer Science, Mathematics, Statistics, Econometrics or a related quantitative field.
• You bring around 5 years of hands-on experience in data science roles in a business context, with prior experience in (energy) trading being considered a plus.
• Proficient with optimization techniques such as linear programming.
• You have experience with building and deploying scalable trading and/or optimization algorithms in the cloud.
• Strong background in Python development: you know how to write modular and clean code, implement unit tests and proper monitoring.
• You understand underlying business problems, can distinguish between main and secondary issues and are able to translate these into robust data driven solutions.
• You have experience with SQL databases, Databricks and streaming data.
• Proactively initiating improvements and your drive to get things done is key.
• 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 are familiar with - parts of - our tech stack:
  Azure Cloud, Kubernetes, Databricks, Airflow, Snowflake.

You will work within the Flex Optimization team as part of the value chain Trading & Structuring, right next to Rotterdam-Alexander station. A complex, dynamic environment where moving markets, ever-changing positions, new technologies, and the law of inhibiting head start create a challenging field of tension which requires a lot of initiative and insight. We interact quickly with each other in an informal, transparent and collegial atmosphere.

In a rapidly changing energy landscape, you and your team will shape the energy transition. A tough job, with challenging but achievable targets.

For more information about the application process, please contact Randolph Mackay, our Senior Recruiter for the Trade Community.