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.
- 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:
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Currently pursuing or recently completed a degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative field.
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Practical experience with Python for data processing (coursework, personal projects, or prior internship acceptable).
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Comfort working with SQL (queries, joins, basic schema concepts) and tabular data (CSV/Excel-style analysis).
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Ability to work independently on defined tasks while asking targeted questions when domain or data ambiguity arises.
Desirable:
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Exposure to pandas, SQLAlchemy, or similar data stack used in ETL pipelines.
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Familiarity with Git, pull requests, and collaborative code review.
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Interest in or prior exposure to shipping, energy commodities, maritime AIS, or geospatial data.
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Experience with CLI tools, environment variables, and cloud/database connectivity (PostgreSQL preferred).
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Basic understanding of automated workflows (e.g. GitHub Actions) or Google Sheets/API integrations.