Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist (VPI)

Vitol
City of London
6 days ago
Create job alert
Overview

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.

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


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Optimisation)

Data Scientist - Tax & Legal

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.