Revenue AI Scientist — Pricing & Network Optimization

Scandinavian Airlines System (SAS); Solna kommun; In loco; SEK 900.000 - 1.300.000; Tempo pieno

Descrizione del lavoro

Scandinavian Airlines System SAS is seeking a Data Scientist focused on Revenue Management and Network Optimization to join our AI Team in Solna. You will design and productionize ML models that drive pricing and network decisions, delivering measurable impact on revenue and commercial performance.

You will work across forecasting, demand modeling, and optimization, building end-to-end solutions and maintaining pipelines in a production environment, with a strong emphasis on scalability and

Competenze

• Master's degree or higher in a math/CS field.
• 3+ years of industry experience in Data Science.
• Translate loosely defined problems into practical ML applications.
• Strong mathematical foundation (linear algebra, calculus, statistics, optimization).
• 3+ years of Python, SQL and ML libraries.
• Proficiency with supervised and unsupervised ML models.
• Experience with Azure cloud computing.

Mansioni

• Develop end-to-end ML solutions for revenue management and network optimization.
• Build and deploy models powering pricing and network decisions with measurable impact.
• Create production-ready pipelines and CI/CD workflows for ML deployments.
• Collaborate with Operations Research and business teams to embed algorithms into initiatives.

Conoscenze

Machine Learning

Optimization

Python

SQL

Deep Learning

Reinforcement Learning

Statistics

Cloud computing

Formazione

Master's degree or higher in a math/CS field

Strumenti

Azure

Descrizione del lavoro

Scandinavian Airlines System SAS is seeking a Data Scientist focused on Revenue Management and Network Optimization to join our AI Team in Solna. You will design and productionize ML models that drive pricing and network decisions, delivering measurable impact on revenue and commercial performance.

You will work across forecasting, demand modeling, and optimization, building end-to-end solutions and maintaining pipelines in a production environment, with a strong emphasis on scalability and