Machine Learning Engineer

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 ‘art of the possible’.

Core Responsibilities include:

• Design, develop, and deploy end-to-end machine learning and data science solutions across our wider business activities (including trading, operations, and support functions) - from raw data ingestion through to production-grade models and monitoring
• Drive adoption and development of the firm’s internal GenAI chat platform as one of the technical leads, extending its capabilities through new integrations, data connectors, and domain-specific prompt engineering; work closely with trading desks and operational teams to identify high-value use cases, embed the tool into day-to-day workflows, and ensure outputs are robust, and trusted by end users.
• Apply a broad range of modelling techniques - including time-series forecasting, NLP, classification, and generative AI - to commodity pricing, supply/demand signals, trade flow analysis, and operational optimization problems
• Own the full data science lifecycle on assigned projects: data sourcing and cleaning, exploratory analysis, feature engineering, model selection and validation, deployment, and ongoing performance monitoring
• Build and maintain robust, well-tested, production-quality code; contribute to shared infrastructure including ML pipelines, data orchestration, and model serving layers
• Integrate ML and GenAI outputs into existing trading systems, dashboards, and workflows; work with software engineers to ensure reliable, scalable adoption across the business
• Communicate analytical findings and model outputs clearly to non-technical stakeholders; present results, assumptions, and limitations in a manner that supports confident commercial decision-making
• Actively participate in code reviews, experiment design, and tooling decisions; mentor colleagues and help raise the overall standard of analytical and engineering practice across the team

• Master’s degree or equivalent in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field
• Fluency in Python for both data science and engineering purposes: clean, modular, well-documented code, with strong understanding of software engineering best practices including version control, testing, and code review
• 5+ years of industry experience developing and deploying machine learning or statistical models, with a proven track record of delivering end-to-end solutions in production environments
• Demonstrable experience applying a broad range of ML methodologies (supervised and unsupervised learning, time-series modelling, NLP/LLMs, optimization) to real-world business problems
• Strong proficiency with ML frameworks (e.g. PyTorch, scikit-learn, Transformers) and experience building or consuming LLM-based pipelines and GenAI applications
• Experience with cloud platforms (AWS preferred) and modern MLOps practices: containerization (Docker/Kubernetes), CI/CD, data pipeline orchestration (e.g. Airflow, Dagster), and model serving
• Strong analytical and problem-solving ability: capable of defining and scoping open-ended problems, proposing sound methodological approaches, and defending modelling choices with rigorous reasoning
• Excellent written and verbal communication skills, with the confidence to present model outputs, caveats, and commercial implications clearly to non-technical audiences including traders and senior management
• Genuine intellectual curiosity about commodities markets, global energy flows, and the commercial dynamics of trading; willingness to develop domain knowledge as part of the role

Desirable Experience

• Experience in the energy or commodities trading industry, with knowledge of financial markets and trading concepts
• Experience surfacing ML outputs through interactive tools (e.g. Dash, Streamlit, or similar) and presenting use cases to non-technical audiences, including traders and senior management
• Time-series modelling in a trading or financial context, including both ML-based and econometric approaches (e.g. ARIMA, cointegration, regime-switching models)
• Data orchestrators (Airflow, Dagster) and cloud-based ETL/ELT pipelines, * A self-motivated individual who thrives on seeing the results of their work make an impact in the business
• Pragmatic and delivery-focused: comfortable navigating ambiguity, balancing rigor with speed, and making sound judgements under uncertainty
• Methodical and detail-oriented: rigorous in experimental design, data validation, and code quality, with a disciplined approach to documenting assumptions and results
• Resourceful, able to think creatively and adapt in a dynamic environment
• Team player, with an open non-political style and a high level of integrity
• Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world’s energy flows

• Competitive salary and benefits package
• Large diversity of projects with real-world impacts on a truly global scale
• Entrepreneurial environment within a flat hierarchy, where great ideas come to life quickly
• Close collaboration with various business units across our key regions (eg. London, Singapore, Houston, Geneva)
• A highly motivated DS and ML team comprised of experienced individuals with a supportive attitude and great team spirit
• Being part of the energy transition through increased emphasis on renewable & alternative energy sources at a pivotal moment in the industry
• Strong management commitment to incorporating machine learning into the future of Vitol’s operations

Vitol is an energy and commodities company with revenues of $400 billion in 2023; its primary business is the trading and distribution of energy products globally - it trades over seven million barrels per day of crude oil and products and, at any time, has 250 ships transporting its cargoes.

Vitol’s clients include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves clients from some 40 offices worldwide and is invested in energy assets globally including 16mm3 of storage, 480kbpd of refining capacity, and 7,000 service stations. To date, we have committed over $2.5 billion of capital to renewable projects, and are identifying and developing low-carbon opportunities around the world. Learn more about us here.

This Role is located in Houston, TX - In office 5x a week