Data Scientist (VPI)

Company Description

Our society is going through a significant energy transition, which will bring continuous change over decades, making the energy ecosystem increasingly complex, and rapidly evolving with new markets, business models, and technologies. In this environment, VPI has a unique opportunity, because we have a strong combination of trading, engineering, and operational capabilities.

We are one of the leading Power Generation operators in the UK with assets across five locations in UK, three sites in Ireland and building a portfolio in Germany. Our total portfolio can generate 3.5 GW of power. We are committed to being part of the UK, Ireland and Germanys pathway to Net Zero. Our efficient and flexible generation portfolio will complement an increased use of renewables as part of the energy mix. This will ensure the security of supply to the power grid and help reduce the UK’s industrial emissions. Key assets in our portfolio are also deploying CO2 abatement programs to reduce their emissions.

We are proud of the role that we play in providing energy across the UK and Ireland and of the role that we will continue to play in the energy system, helping to bridge the gap between the now and the next.

Our people are our business. Talent is precious to us, and we create an environment in which individuals can reach their full potential, unhindered by hierarchy. We are committed to developing and sustaining a diverse workforce.

For more information: vpi.energy/about/

Job Description

We are looking for a highly motivated Data Scientist to join our team and to work closely with our power trading desk. The ideal candidate will leverage their expertise in data modelling, statistical analysis, and machine learning to extract valuable insights from complex datasets, driving data-informed decision-making across the organization. You will work closely with cross-functional teams to develop and implement models, improve business processes, and deliver impactful data-driven solutions.

Key Responsibilities:

Being an energetic and enthusiastic leader that bridges the power trading desk and trading technology team, who combines strategic vision with hands on capability to use data, analytics and advanced modelling Designing and implementing data enabled models and applications, to deliver measurable value via use cases such as: optimisation, valuation, and trading of power assets, including batteries, renewables and thermal plants Collaborating with traders and applying expertise in UK and EMEA power markets, to build both fundamentals and data driven automated trading strategies, with statistically sound and robustly evidenced back testing Build and integrate production quality solutions (potentially including generative AI) into VPI’s cloud to provide robust, scalable services in line with DevOps and MLOps principles Being a leader with ownership and responsibility over data science projects, while also being a strong independent contributor to other technology and trading workstreams in a flat, collaborative environment Performing and supervising Exploratory Data Analysis with traditional and alternative types of data, extracting insights, visualising and communicating the results in a trading desk relevant context Providing cross functional mentorship of data science and advanced modelling techniques using Python, becoming a centre of excellence that sets and demonstrates best practices and standards across VPI Actively participating in code reviews, experiment design and tooling decisions to help drive the velocity and quality of data science work

Qualifications

Master’s degree in computer science, Machine Learning, or a related field. Ph.D. is a plus Fluency in Python with the ability to write clean, modular, well-documented code as well as a solid understanding of coding best practices 5+ years in industry developing and deploying production quality machine learning models in line with MLOps principles Experience in power markets, with knowledge of financial markets and trading concepts Experience with back testing techniques appropriate to financial market applications Experience exploring and extracting insights from heterogeneous multi-dimensional data sets, and presenting complex data visually Time series modelling (both machine-learning and econometric approaches) Familiarity with cloud platforms (AWS) and containerisation technologies (Docker) Familiarity with cloud-based ETL/ELT data pipelines and orchestrators (Airflow, Dagster, Prefect) Excellent problem-solving skills, ability to work independently and in a team Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders Understanding of ML fundamentals and experience with ML frameworks Advanced coursework in math, statistics, and machine learning preferred Demonstrable attention to detail