Data Scientist | Data Engineering | Python & SQL | Business Intelligence

Role Overview

As a Data Scientist, you will play a key role in building and managing analytical, predictive, and data-driven capabilities across market intelligence, operational performance, commercial insights, and organizational analytics. The role involves developing scalable data systems, predictive models, dashboards, and automation solutions to strengthen business decision-making and support the organization's digital transformation initiatives.

Key Responsibilities:

• Design and manage unified, structured databases across market, operational, and organizational datasets.
• Build scalable, analytics-ready data pipelines and improve data integrity, standardization, and enterprise-wide data accessibility.
• Develop statistical and predictive models for market forecasting, pricing intelligence, fuel and bunker trends, voyage economics, and operational performance.
• Conduct trend analysis, correlation studies, and scenario modelling to generate actionable business insights.
• Collaborate with Finance and Strategy teams to support business planning, risk management, and board-level reporting.
• Build dashboards and decision-support tools using Power BI and AI-based analytics platforms.
• Drive automation across reporting and analytics workflows to improve efficiency and accuracy.
• Apply machine learning techniques to enhance forecasting accuracy, analytical capabilities, and decision-making speed.
• Work closely with Chartering, Operations, Technical, Strategy, Finance, and Derivatives teams to understand business requirements.
• Translate business challenges into scalable analytical solutions and present insights effectively to senior leadership.

Requirements

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, or a related field.
• Minimum of 5 years of hands-on experience in analytics, data engineering, and business intelligence.
• Strong proficiency in SQL, Python, and data visualization tools such as Power BI or Tableau.
• Practical experience in machine learning, statistical analysis, and predictive modelling.
• Familiarity with APIs, cloud databases, and workflow automation tools.
• Exposure to the shipping, logistics, or commodities industry will be an advantage but is not mandatory.
• Strong analytical thinking, problem-solving, and stakeholder management skills.
• Ability to work independently, manage multiple priorities, and translate complex data into meaningful business insights.