Senior Data Scientist, Applied ML

Vitol’s Applied ML team in Houston works directly with the trading desks to find commercial value through data and applied data science . Projects start with a trader’s or analyst’s question, not with a model.

The work is fast and phased, and it is judged on one thing: whether the desk uses it to make better decisions.

About Vitol

• The Environment Vitol is one of the world’s largest independent energy traders, and Houston is one of its core trading offices.
• The Applied ML team sits close to the desks, so feedback is fast and the people using your work are just a few desks away.
• Vitol is a leader in energy and commodities.
• Vitol produces, manages and delivers energy and commodities to consumers and industry worldwide.
• In addition to its primary business of trading, Vitol is invested in infrastructure globally, with $10+billion invested in long-term assets.
• Vitol’s customers include national oil companies, multinationals, leading industrial companies and utilities.
• Founded in Rotterdam in 1966, today Vitol serves its customers from more than 40 offices worldwide.
• Revenues in 2025 were $343bn.
• 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.
• Our team comprises more than 65+ nationalities and we are committed to developing and sustaining a diverse work force.

Responsibilities

• You will work alongside traders and analysts from the first conversation through to production and beyond.
• You frame the problem with them, review results with them as the work progresses, and stay with the tool after it ships.
• You own both the science and its path to production.
• You Will Do Work side by side with traders and analysts to turn market questions into scoped projects, each with a clear commercial hypothesis and a defined go/no-go point.
• Review progress with the desk throughout each project, not just at handoff.
• Show intermediate results early, test them against the desk’s market knowledge, and change course when the signal or the priority shifts.
• LLM, NLP and multimodal pipelines that extract structured signals from unstructured sources such as outage reports, news, filings, and web content.
• Design and run quick experiments to test feasibility across modelling approaches, and stop weak ideas early.
• Build models that predict outage impacts, classify disruption patterns, and detect anomalies and regime changes in fundamental data.
• Take the models that prove out into production.
• That means packaged, tested, deployed through CI/CD, with monitoring and drift detection built in from day one.
• Standalone dashboards are not the default.
• Source data through the Vision platform.
• When you need a new source, work with the Vision team rather than building side pipelines, and follow the existing web-scraping architecture.
• Evaluate commercial AI platforms and tools against real desk problems, and give a clear recommendation.
• Explain results, uncertainty, and limits in plain terms to traders, analysts, and senior stakeholders.
• Hands-on experience building extraction and classification pipelines with LLMs, covering evaluation, prompt and retrieval design, and cost and latency trade-offs.
• Python with scikit-learn, PyTorch, and HuggingFace.
• You write production-quality code that others can review and maintain.
• Docker, AWS, CI/CD, and MLOps practice, including model versioning, monitoring, and retraining.
• Your production models are monitored, documented, and supportable by the wider team.
• Failures are found by monitoring, not by a trader.
• Failed experiments are stopped early and written up, so the team does not repeat them.
• You have built reusable components, such as extraction pipelines and evaluation harnesses, that the rest of the team uses.

Requirements

• Teams, alerts, and the Vision Excel and Python tooling.
• 5+ years applying machine learning and NLP to messy, real-world data, including models you took to production and supported after launch.
• You have worked directly with commercial users in trading, finance, or a similar fast-paced setting, and you can point to decisions your work changed.
• Master’s or PhD in statistics, mathematics, physics, engineering, or equivalent depth gained in industry.
• Strong statistics and experimental design.
• Experience with anomaly detection and with combining signals across multiple time-series and text sources.
• You can explain what a model means for a trade in two sentences, and you are comfortable being challenged by people who know the market better than you do.
• Success Looks Like in Year One Traders and analysts on at least two desks bring problems to you directly.
• At least two proof-of-value projects reach a go/no-go decision, and at least one is in production and in regular use on the desk.
• We look for scientists and engineers who own outcomes, not just tickets.
• You will have room to choose your approach, and you will be expected to deliver work that traders can rely on.

Preferred

• Knowledge of energy or commodities markets, graph modelling, sentiment analysis, or agentic workflows and MCP.

Benefits

• Medical Dental Vision Paid Vacation 401k with company contributions Life insurance Short-term & Long-term disability All your information will be kept confidential according to EEO guidelines.

Learn more about us here . This role is located in Houston, TX onsite 5x a week