5 months 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.
- Lead technical development and adoption of the internal GenAI chat platform through integrations, data connectors, domain-specific prompt engineering, and business workflow integration.
- Apply time-series forecasting, NLP, classification, optimization, and generative AI to commodity pricing, supply and demand, trade flow, 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, model-serving layers, and shared infrastructure.
- Integrate ML and GenAI outputs into trading systems, dashboards, and business workflows in collaboration with software engineers.
- Communicate model outputs, assumptions, limitations, and commercial implications to traders, senior management, and other non-technical stakeholders.
- Participate in code reviews, experiment design, and tooling decisions while mentoring colleagues and improving analytical and engineering practices.
Requirements
- Master’s degree or equivalent in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
- At least 5 years of industry experience developing and deploying machine learning or statistical models in production.
- Fluency in Python for data science and engineering, including software engineering best practices, version control, testing, and code review.
- 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, plus experience with LLM-based pipelines and GenAI applications.
- Experience with cloud platforms, preferably AWS, and modern MLOps practices including Docker, Kubernetes, CI/CD, data orchestration, and model serving.
- Strong analytical, problem-solving, written communication, and verbal communication skills.
- Ability to define open-ended problems, propose rigorous approaches, defend modeling choices, and explain results to commercial stakeholders.
- Interest in commodities markets, global energy flows, and trading dynamics, with willingness to develop relevant domain knowledge.
- Preferred experience includes energy or commodities trading, financial markets, interactive ML tools such as Dash or Streamlit, trading-focused time-series and econometric modeling, and cloud ETL/ELT pipelines.
Benefits
- Competitive salary and benefits package.
- Large diversity of projects with real-world impact at global scale.
- Entrepreneurial environment with a flat hierarchy and rapid idea development.
- Collaboration with business units across London, Singapore, Houston, and Geneva.
- Supportive, experienced data science and machine learning team.
- Opportunity to contribute to Vitol’s energy transition and renewable and alternative energy initiatives.
- Strong management commitment to incorporating machine learning into Vitol’s operations.
- The role is located in Houston, Texas, and requires working fully in the office five days per week.
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