Senior Data Scientist — Port Operations Optimisation

Tanya Osei

Contract · Senior · Amsterdam

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About the role

We are a logistics and port operations company headquartered in Rotterdam. We handle container terminal operations, inland barge coordination, and truck gate management across five terminals in the Port of Rotterdam and two inland hubs. In a typical week, we coordinate the movement of approximately 140,000 TEU of container cargo through our terminals. Every container that sits in the wrong position in our yard, every truck that arrives at the gate three hours early, every barge that waits for a berth that could have been pre-assigned costs us money and costs our clients time. The optimisation opportunity is significant and we know it. We have been building our data infrastructure for three years and we now have the data quality to do serious analytical work. We are hiring a Senior Data Scientist on a 12-month contract to work on three specific problems: berth scheduling optimisation for our barge operations, yard crane productivity modelling, and truck gate arrival prediction. These are well-defined operational problems with clear business value metrics. We need someone who can turn data into decisions, not just models.

Responsibilities

Build a barge berth scheduling optimisation model that reduces average barge waiting time across our five Rotterdam terminals
Develop a yard crane productivity model that predicts equipment utilisation bottlenecks from container movement and yard position data
Build a truck gate arrival prediction model to enable proactive gate staffing and appointment slot management
Deliver analysis and model outputs in operational reporting formats that terminal managers can use in daily planning meetings
Conduct structured knowledge transfer to the permanent analytics team before end of contract

Requirements

6+ years of data science with experience in operations research, supply chain analytics, or logistics optimisation
Python for modelling, simulation, and building the analytical pipelines that operational planners can rely on
Apache Spark for processing our container movement, gate transaction, and equipment telemetry data at scale
SQL for working with our terminal operating system database and our data warehouse — most of the feature engineering work lives in SQL
Statistics: time-series forecasting, simulation, and the ability to quantify the operational value of a model improvement in terms a terminal manager can act on
Scikit-learn for building prediction models with the interpretability that operational users need to trust and use them
Familiarity with port operations, maritime logistics, or supply chain optimisation is a genuine advantage — domain knowledge accelerates everything

Benefits

€720 – €880 per day (contract rate)
12-month initial contract
Remote-first with two days per month at our Rotterdam headquarters — we cover travel
Work on a physical, operational problem where your model directly affects the schedule a terminal runs on the next morning
Access to a rich operational dataset from one of the largest container ports in the world