Revenue Optimization Data Scientist | AI for Pricing

SAS - Scandinavian Airlines; Solna kommun; On-site; SEK 700,000 - 1,000,000; Full time

Benefits offered by this job

Travel perks

Health & wellness

Modern Frösundavik office

Job summary

SAS - Scandinavian Airlines is investing in AI and advanced analytics to transform revenue management and network optimization. As a Data Scientist focused on optimization, you will shape pricing, forecasting, and decision-making that drive commercial performance and customer value.

You will design, train, and productionize ML models powering SAS revenue optimization, collaborating with the Operations Research team and pushing scalable solutions from research to production in a cloud-first

Qualifications

• Master's degree or higher in Data Science, Statistics, Econometrics, or related field.
• Minimum 3 years of industry experience in Data Science.
• Proven ability to translate loosely defined problems into practical applications.
• Strong mathematical background (linear algebra, calculus, statistics, optimization).
• ≥3 years in Python, SQL, and popular ML/DL libraries.
• Experience with cloud computing; Azure preferred.

Responsibilities

• Turn business challenges into analytical problems and build end-to-end ML solutions.
• Design, train, and maintain models powering revenue optimization.
• Develop demand forecasting, purchase probability, and revenue optimization models.
• Collaborate with Operations Research to deploy algorithms into production.

Skills

Python

SQL

Machine learning

Statistics

Optimization

Azure

Data analysis

Jupyter

Education

Master's degree or higher in a relevant field

Tools

Git

TensorFlow

scikit-learn

Jupyter

Job description

SAS - Scandinavian Airlines is investing in AI and advanced analytics to transform revenue management and network optimization. As a Data Scientist focused on optimization, you will shape pricing, forecasting, and decision-making that drive commercial performance and customer value.

You will design, train, and productionize ML models powering SAS revenue optimization, collaborating with the Operations Research team and pushing scalable solutions from research to production in a cloud-first