Senior Data Scientist, Applied ML

Vitol•Houston, TX

14h•Onsite

About The Position

Vitol’s Applied ML team in Houston works directly with trading desks to find commercial value through data and applied data science. Projects begin with questions from traders or analysts, and the Senior Data Scientist works with them from problem framing through production and ongoing support. The role owns both the science and its path to production, with success measured by whether desk users rely on the resulting tools to make better decisions. The work involves building LLM, NLP, and multimodal pipelines to extract structured signals from unstructured sources, and developing models for outage impacts, disruption patterns, anomalies, and regime changes in fundamental data. Proven models are packaged, tested, deployed through CI/CD, and supported with monitoring and drift detection. Insights are delivered through tools traders already use, including Teams, alerts, and Vision Excel and Python tooling. The role is onsite in Houston five days a week.

Requirements

At least 5 years of experience applying machine learning and NLP to messy, real-world data, including taking models to production and supporting them after launch.
Experience working directly with commercial users in trading, finance, or a similar fast-paced setting, with examples of decisions influenced by the work.
A master’s or PhD in statistics, mathematics, physics, engineering, or equivalent depth gained in industry.
Strong statistics and experimental design skills.
Hands-on experience building LLM extraction and classification pipelines, including evaluation, prompt and retrieval design, and cost and latency trade-offs.
Python experience with scikit-learn, PyTorch, and HuggingFace.
Ability to write production-quality code that others can review and maintain.
Experience with Docker, AWS, CI/CD, and MLOps practices, including model versioning, monitoring, and retraining.
Experience with anomaly detection and combining signals across multiple time-series and text sources.
Ability to explain what a model means for a trade clearly and to engage with challenges from people with deeper market knowledge.

Nice To Haves

Knowledge of energy or commodities markets.
Experience with graph modelling.
Experience with sentiment analysis.
Experience with agentic workflows and MCP.

Responsibilities

Work with traders and analysts to turn market questions into scoped projects with clear commercial hypotheses and defined go/no-go points.
Review progress with desk users throughout projects, share intermediate results, test them against market knowledge, and adjust direction as signals or priorities change.
Build LLM, NLP, and multimodal pipelines that extract structured signals from unstructured sources such as outage reports, news, filings, and web content.
Design and run experiments to test modelling feasibility and stop weak ideas early.
Build models to predict outage impacts, classify disruption patterns, and detect anomalies and regime changes in fundamental data.
Take successful models into production, including packaging, testing, CI/CD deployment, monitoring, and drift detection.
Deliver insights through Teams, alerts, and Vision Excel and Python tooling rather than defaulting to standalone dashboards.
Source data through the Vision platform; coordinate with the Vision team for new sources and follow the existing web-scraping architecture.
Evaluate commercial AI platforms and tools against real desk problems and provide clear recommendations.
Explain results, uncertainty, and limitations to traders, analysts, and senior stakeholders in plain language.
Support production models after launch and ensure they are monitored, documented, and supportable by the wider team.
Write up failed experiments and build reusable components, such as extraction pipelines and evaluation harnesses.