Responsibilities
- 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
Requirements
- Experience in designing, developing, and deploying end-to-end machine learning and data science solutions
- Ability to work with business stakeholders to define projects, process data, perform exploratory analysis, select and tune models, and implement production models
- Proven experience applying machine learning techniques such as time-series forecasting, NLP, classification, and generative AI
- Ownership of the full data science lifecycle including data sourcing, cleaning, feature engineering, model validation, deployment, and monitoring
- Strong coding skills with emphasis on building robust, well-tested, production-quality code
- Experience integrating ML models into production systems, dashboards, or workflows
- Ability to communicate technical findings clearly to non-technical stakeholders
Nice to Have
- Experience as a technical lead in driving adoption and development of internal GenAI platforms
- Experience with domain-specific prompt engineering and extending GenAI capabilities via integrations and data connectors
- Collaborative experience working with trading desks and operational teams on high-value use cases
- Prior work in the energy or commodities trading industry
