Data Scientist

Kpler S.A.S.

Job Overview

Location

Hungary

Job Type

Full-time

Full Job Description

📋 Description

• Develop and implement machine learning models and algorithms to aggregate and analyze data from Gas and Power teams, enabling a unified view of future forecasts including storage, pipeline, and consumption trends.
• Enhance Kpler’s cross-commodity offering by integrating disparate data sources into cohesive analytical frameworks that support client decision-making in energy markets.
• Collaborate with domain experts across Gas and Power teams to understand data structures, identify key variables, and translate business needs into technical requirements for predictive modeling.
• Build scalable data pipelines to process high-volume, heterogeneous datasets from global trade, maritime, and energy sources, ensuring data quality and consistency for forecasting applications.
• Create visualizations and dashboards to communicate complex analytical insights to non-technical stakeholders, improving accessibility of energy market forecasts.
• Continuously evaluate model performance using statistical metrics and refine algorithms to improve accuracy, reliability, and relevance of forecasts over time.
• Work within a cross-functional team of data engineers, analysts, and domain specialists to align model outputs with product roadmaps and client demands in commodities and energy sectors.
• Stay current with advancements in data science, energy market analytics, and machine learning techniques to apply innovative solutions to Kpler’s global trade intelligence platform.
• Ensure all models and analyses comply with internal data governance standards and maintain reproducibility through version-controlled code and documented workflows.
• Support the expansion of Kpler’s predictive capabilities by identifying new data sources, features, and methodologies that enhance the value of the Gas and Power offering.
• Contribute to knowledge sharing within the data science team through documentation, code reviews, and peer mentoring to elevate team-wide analytical standards.
• Translate ambiguous business questions into well-defined data science problems with measurable outcomes and clear success criteria.
• Participate in sprint planning, backlog grooming, and iterative development cycles to deliver incremental improvements to forecasting models and user-facing tools.

🎯 Requirements

• Proficiency in Python for data analysis and machine learning, with experience using libraries such as pandas, scikit-learn, and NumPy
• Demonstrated experience building predictive models for time-series forecasting in energy, commodities, or related domains
• Strong understanding of data pipelines and ETL processes for handling large-scale, heterogeneous datasets
• Experience collaborating with cross-functional teams to translate business needs into technical solutions
• Ability to communicate complex technical results to non-technical stakeholders through clear visualizations and narratives
• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field

🏖️ Benefits

• Opportunity to work on high-impact data science projects at the intersection of global trade and energy transition
• Access to proprietary, high-quality datasets from global maritime and commodities markets
• Collaborative, international team environment with experts from 69 countries
• Support for professional development and continuous learning in data science and energy analytics

Skills & Technologies

Data Science

Onsite

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Kpler S.A.S.

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About Kpler S.A.S.

Kpler S.A.S. provides real-time and historical data on commodity flows, tracking global shipments of crude oil, refined products, liquefied natural gas, metals, and agricultural goods. The company aggregates satellite, customs, and port data into a web-based analytics platform used by traders, producers, shippers, and financial institutions to monitor supply chains, assess inventories, and forecast market balances. Founded in 2014 and headquartered in Paris, Kpler employs proprietary algorithms and a global network of sources to deliver granular cargo-level information, enabling clients to make informed trading, logistics, and risk-management decisions across energy and bulk commodity markets.

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