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• Data Scientist

• Permanent
• Full time
• 9000-059, Funchal, Madeira, Portugal
• Product & Data

As a Data Scientist at OceanScore, you work on applied modelling challenges at the intersection of maritime operations, environmental regulation, and large-scale data analysis. Your work contributes directly to the analytical engines that power OceanScore’s solutions used by shipping companies, ports, and other maritime stakeholders.

You will design, build, and continuously improve models that analyse vessel activity, forecast operational scenarios, and quantify emissions and regulatory exposure. Typical modelling problems include vessel behaviour forecasting, emissions exposure modelling, and regulatory scenario analysis using large-scale maritime datasets.

Working closely with product, commercial, and external development teams, you turn analytical models into practical tools used by customers and internal teams. Your work directly shapes how maritime data is interpreted within our platform and helps stakeholders make better operational and regulatory decisions.

Please note this is an on-site role in our Funchal, Madeira Office and proficiency in Portuguese is mandatory.

Your Key Responsibilities

• Applied Data Modelling: You design, develop, and continuously improve analytical models related to maritime activity, emissions, and operational performance using large-scale time-series and geospatial datasets.

• Forecasting & Scenario Analysis: You build forecasting and scenario models that analyse vessel behaviour, regulatory exposure, and operational outcomes under different assumptions.

• Maritime Data Analysis: You work with complex datasets including AIS vessel data, voyage profiles, and emissions data to derive structured insights and modelling inputs.

• Model Integration & Collaboration: You collaborate closely with product managers, commercial teams, and external developers to ensure models are integrated into OceanScore’s solutions and deliver practical value.

• Continuous Model Improvement: You Review and refine modelling approaches to improve accuracy, robustness, and transparency as data availability and product requirements evolve.

What you bring:

• You have 3–5 years of experience working independently with complex analytical models and large datasets.

• You have strong programming skills in Python (pandas, numpy, scikit-learn, pytorch) and SQL, and are comfortable building reproducible analytical workflows and working with structured data environments.

• You have experience with time-series data and forecasting or scenario modelling, ideally in operational or real-world contexts.

• You are experienced in using workflow automation and AI-supported tools to improve processes and are curious about how emerging AI capabilities can be applied in day-to-day work.

• You are familiar with large-scale data environments and understand how analytical models interact with data pipelines, ETL processes, and distributed data processing frameworks such as Spark.

• Any familiarity with geospatial analytics (geopandas, shapely) and /or platforms such as Databricks is a plus.

How you work

• You approach problems in a structured and logically rigorous way.

• You are comfortable working with ambiguous or incomplete datasets and iterating toward practical solutions.

• You communicate your models, assumptions, and results clearly so colleagues from different disciplines can understand and use them.

• You take ownership of your modelling topics and work independently while maintaining visibility of your work.

• You are curious and enjoy questioning assumptions behind models and data to improve outcomes.

Not 100% qualified? Apply anyway.

We know great people don’t always check every box. If you’re passionate about what we’re building and think you could make an impact, we want to hear from you. You might bring something we didn’t even know we needed.

Success will be measured by:

• Robust and transparent models that strengthen OceanScore’s analytical capabilities.

• Practical modelling outputs that help internal teams and customers make better operational decisions.

• Continuous development and refinement of modelling approaches as new data and requirements emerge.