5 hours ago
Houston, TX, USASenior
Responsibilities
- Design, develop, deploy, and monitor end-to-end machine learning and data science solutions across trading, operations, and support functions.
- Develop the internal GenAI platform through integrations, data connectors, domain-specific context engineering, and workflow adoption.
- Design and deploy agentic LLM workflows using tool use and function calling, including reusable prompts and AI Skills.
- Evaluate and benchmark LLM and agentic solutions and refine them based on stakeholder feedback.
- Apply time-series forecasting, NLP, classification, generative AI, and optimization techniques to commodity and operational problems.
- Own data sourcing, cleaning, exploratory analysis, feature engineering, model selection, validation, deployment, and ongoing performance monitoring.
- Build production-quality Python code and contribute to ML pipelines, data orchestration, and model-serving infrastructure.
- Integrate ML and GenAI outputs into trading systems, dashboards, and business workflows.
- Communicate model outputs, assumptions, limitations, and commercial implications to non-technical stakeholders.
- Participate in code reviews, experiment design, and tooling decisions while mentoring colleagues and improving team practices.
Requirements
- Master’s degree or equivalent in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
- Fluency in Python for data science and engineering, including modular code, version control, testing, and code review practices.
- At least 5 years of industry experience developing and deploying machine learning or statistical models in production.
- Demonstrable experience with supervised and unsupervised learning, time-series modeling, NLP/LLMs, and optimization.
- Strong proficiency with ML frameworks such as PyTorch, scikit-learn, and Transformers.
- Hands-on GenAI experience including prompt engineering, agentic patterns, tool use, and function calling.
- Experience with AWS or other cloud platforms and MLOps practices including Docker, Kubernetes, CI/CD, orchestration, and model serving.
- Strong analytical, problem-solving, written, and verbal communication skills.
- Ability to work with non-technical stakeholders, including traders and senior management.
- Interest in commodities markets, global energy flows, and trading dynamics.
- Preferred experience in energy or commodities trading, financial markets, interactive tools such as Dash or Streamlit, trading-oriented time-series modeling, RAG pipelines, multi-agent systems, MCP servers or clients, Airflow or Dagster, cloud ETL/ELT pipelines, LLM evaluations, or model benchmarking.
Benefits
- Medical, dental, and vision coverage.
- Paid vacation, 401(k) with company contributions, life insurance, and short-term and long-term disability coverage.
- Large-scale projects with real-world impact and collaboration across London, Singapore, Houston, Geneva, and other regions.
- Supportive, experienced data science and machine learning team in an entrepreneurial, flat-hierarchy environment.
- Opportunity to contribute to energy transition initiatives and the future use of machine learning at Vitol.
- Houston, Texas location with onsite work five days per week.
Tech Stack
Categories
About Vitol
Vitol trades, transports, and distributes energy and commodities—from crude and refined products to gas, power, renewables and carbon credits—for refineries, utilities, airlines, retailers and other traders. It also operates and invests in supporting infrastructure, including shipping, terminals and storage, and power assets. Founded in 1966 and privately held, Vitol is headquartered in Rotterdam and runs a global business from 40+ offices.