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Join the Liquids Data Operations team to help scale our bunker tracking product from existing established hubs to additional bunkering centres worldwide.
This is a hands-on data analytics internship with exposure to shipping/commodities domain logic, geospatial filtering, and operational automation.
Key Responsibilities
- Increase Geographical Coverage: Expand the current bunker tracking model (python-based) to other hubs, including conducting exploratory work to fine-tune for each hub’s nuances
- Perform Routine Backtesting: Validate modelled bunker activity by exporting analysis-ready CSVs and reconciling monthly estimates against official port statistics, industry publications, or other trusted external sources.
- Investigate Data Quality Issues: Routinely analyze the bunker tracking data set to identify structural data quality issues and develop improvement solutions in the pipeline / post-processing to resolve them.
- Improve Pipeline & Data Platform: Help mature how we develop, run and persist bunker pipelines so that expansion is maintainable and scalable.
Essential:
Currently pursuing or recently completed a degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative field.
Practical experience with Python for data processing (coursework, personal projects, or prior internship acceptable).
Comfort working with SQL (queries, joins, basic schema concepts) and tabular data (CSV/Excel-style analysis).
Ability to work independently on defined tasks while asking targeted questions when domain or data ambiguity arises.
Desirable:
Exposure to pandas, SQLAlchemy, or similar data stack used in ETL pipelines.
Familiarity with Git, pull requests, and collaborative code review.
Interest in or prior exposure to shipping, energy commodities, maritime AIS, or geospatial data.
Experience with CLI tools, environment variables, and cloud/database connectivity (PostgreSQL preferred).
Basic understanding of automated workflows (e.g. GitHub Actions) or Google Sheets/API integrations.
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