As our portfolio of work continues to grow, we are looking for an experienced Machine Learning Engineer to join our data science and machine learning team. The individual will work closely with the data and machine learning specialists, software engineers and commercial teams to deliver machine learning models and applications. We work across the trading business, operations, and other support functions; so the individual will need to be comfortable working with a variety of stakeholders and technologies.
The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing, exploratory analysis, model selection and tuning, and implementation of production models.
The successful candidate will join a team of experienced, collaborative practitioners, who are (pragmatically) solving some of the most challenging and impactful problems the energy industry is facing; as well as pushing the boundaries around the āart of the possibleā.
Core Responsibilities include:
- Being an energetic and enthusiastic member of a team uniquely positioned to bring the power of data to bear on the inner workings of the energy industry
- Ability to relate effortlessly with the commercial side of the business, being a thought leader and looking for new ways how AI/DS can create value for Vitol
- Leading design, development, and deployment of machine learning systems, bringing their technical knowledge and experience into the team, and using this to create real solutions to real problems.
- Prioritization of projects based on changing business conditions and a keen eye for where maximum value can be delivered
- Collaborating with cross-functional technology teams to gather requirements and ensure the solutions align with business objectives
- Helping integrate the teamās solutions (including GenAI) into existing systems and platforms to provide seamless user experiences and scale adoption
- Actively participating in code reviews, experiment design and tooling decisions to help drive the teamās velocity and quality
- Helping build data and machine learning expertise within the business through knowledge sharing