Scandinavian Airlines System Denmark -Norway-Swe jobb

Company description: Scandinavian Airlines System SAS Job description: Data Scientist Focused on Revenue Management & Network Optimization At SAS, we're passionate about transforming aviation with cutting-edge technology. As part of our ongoing Digital & IT transformation, we are expanding our investments in AI, machine learning, advanced analytics, and data-driven decision-making to create smarter customer experiences and stronger commercial performance. As part of our AI Team, you'll shape the future of air travel by creating innovative, data-driven solutions that maximize revenue.  You will work on one of the most challenging and rewarding problems in the industry: finding the right price, for the right customer, at the right time. Your work will ensure that the airline's Revenue Optimization remains scientifically robust, operationally scalable, and commercially impactful, directly shaping how revenue is optimized in a dynamic, competitive marketplace.  Your responsibilities will center on turning business challenges into well-defined analytical problems and building end-to-end machine learning solutions that deliver measurable impact. You will design, train, and maintain models that power SAS's revenue optimization, ensuring they are accurate, scalable, and production-ready.  A priority will be to deliver dynamic pricing, a groundbreaking shift in how SAS competes in the marketplace. At the same time, we are developing AI-driven revenue optimization at scale, ensuring our models continuously learn, adapt, and evolve with industry dynamics.  Your work will help SAS unlock the full potential of AI in airline revenue optimization, making smarter pricing and network decisions a reality today.  Challenges You Will Work On  Machine Learning & Optimization Techniques  Build and deploy algorithms that power revenue and network decisions. Among other solutions, you'll develop demand forecasting, purchase probability, and revenue optimization models, leveraging a broad spectrum of AI methods.  From classical ensemble methods to deep learning architectures and reinforcement learning frameworks, you'll select the most effective approach for each complex problem.  Operations Research  Beyond building models, you'll explore new formulations, test novel algorithms and findings that push the boundaries of airline revenue optimization.  You will convert advanced research into production-ready optimization and modeling solutions, collaborating with the Operations Research team to embed novel algorithms into initiatives that deliver measurable business impact.  MLOps  Leverage end-to-end ML infrastructure to take models from research to production at scale. This includes designing reproducible training and inference pipelines, integrating CI/CD workflows for seamless deployment, and ensuring robust monitoring to detect drift and trigger automated retraining, keeping models stable and accurate in production.   To be successful we believe you have Master's Degree or higher with strong mathematics focus (Data Science, Statistics, Econometrics, Applied Mathematics, Optimization, Computer Science) or related field.  At least 3 years of industry experience within the Data Science domain.  Proven record of taking on loosely defined problems and translating them into practical applications.  Strong mathematical background (e.g., linear algebra, calculus, statistics, optimization).  More than 3 years' experience with Python, SQL and popular machine/deep learning libraries.  Proficient with statistical and machine learning models (both supervised and unsupervised learning).  Well-versed in cloud computing. Prior experience with Azure is a big plus.  We believe you share our passion for data and algorithms. You're self-driven, persistent, creative, and curious, with a strong passion for learning. You seek to understand both the big picture and the intricate details that make it up.  Like us, you get excited by commercial opportunities and proactively strive to create business value.  Additional information Apply by November 16, 2026. We review applications continuously and encourage you to apply early, as the position may be filled before the closing date. Please note that, due to GDPR regulations, we cannot accept applications via email. This is a full-time position (100%) based at our headquarters in Frösundavik, Solna, Stockholm. We value in-person collaboration, although flexible working arrangements may be available depending on team and business needs. As this role contributes to security-classified systems and business-critical capabilities, a background check will be conducted as part of the final stage of the recruitment process. Please note that applications must be submitted through our careers site, as we are unable to process applications received by email.

Company description: Scandinavian Airlines System SAS Job description: Challenges you will work on At SAS, machine learning has moved well beyond experimentation. As part of our ongoing Digital & IT transformation, we are expanding our investments in machine learning, automation, data platforms, and Generative AI. We design, deploy, and operate production models that support pricing decisions, personalize customer experiences, and make our operations smarter every day. As a Machine Learning Engineer, you will play an important role in developing, deploying, and maintaining machine learning solutions that create real business value. You will work closely with data scientists, data engineers, and AI engineers to help bring models from development into production and ensure they perform reliably at scale. If you're passionate about applying machine learning to real-world challenges and want to help shape the future of AI at SAS, this is the role for you! As our Machine Learning Engineer, you will help develop and operationalize machine learning solutions across SAS. In this role, you will work hands-on with deployment, monitoring, and continuous improvement of production ML solutions. You will collaborate closely with data scientists to transition models from experimentation to production, help improve deployment processes and contribute to the ongoing development of our ML platform and engineering practices. Key Responsibilities Design, develop, test, deploy and monitor end-to-end pipelines for machine learning models in Azure. Apply MLOps best practices, including version control, continuous integration, continuous delivery, continuous training, testing, and monitoring, to ensure the quality and reliability of the machine learning solutions. Monitor model performance and help identify issues related to model quality, drift, and reliability. Support automation of model training, deployment, and retraining processes. Contribute to best practices for machine learning engineering, testing, documentation, and deployment. Work with Data Engineering and IT teams to ensure solutions are scalable, secure, and reliable. Help develop and improve CI/CD processes for machine learning applications. Participate in troubleshooting and resolving issues in production ML systems. Stay up to date with developments in machine learning, MLOps, and cloud technologies. The Team We are a central AI & Automation function within SAS Digital & IT that develops and operates solutions across machine learning, automation, MLOps, and Generative AI. Our team consists of data scientists, AI engineers, ML engineers, and automation specialists who work together to deliver solutions that create measurable business impact. As SAS continues to strengthen its AI capabilities, we are investing in modern cloud platforms, scalable AI solutions, and engineering practices that enable machine learning to create value across the business. As part of the team, you will contribute to a growing portfolio of machine learning products and collaborate across disciplines to ensure solutions are production-ready, scalable, and valuable to the business. To be successful we believe you have Master's Degree in Computer Science, Engineering, Machine Learning, Mathematics, or a related field. 2-5 years of experience in Machine Learning Engineering, MLOps, or a related role. Experience developing, deploying, and operating machine learning solutions in production environments. Hands-on experience with Azure ML, Azure Databricks, Azure Data Factory, or similar cloud-based ML platforms. Proficiency in Python, SQL, Git, and Bash. Experience deploying and serving machine learning models through APIs, as well as monitoring model performance in production. Understanding of model evaluation techniques, hyperparameter tuning, and validation approaches such as back-testing. Familiarity with Infrastructure as Code, preferably Terraform on Azure. Solid understanding of software engineering practices, including testing, version control, and CI/CD principles. Strong communication skills and the ability to explain and document technical concepts clearly and effectively. Experience with Generative AI, LLMs, or AI agents is considered a plus. We believe you are a curious and pragmatic engineer who enjoys solving problems with data and machine learning. You are comfortable working across the ML lifecycle, from experimentation to production, and enjoy collaborating with colleagues from different disciplines. You are eager to learn, improve your technical skills, and contribute to building reliable AI solutions that create real business value. Most importantly, you understand that successful machine learning is not only about models, but about delivering solutions that work in the real world and continue to provide value over time.   Additional information Apply by November 16, 2026. We review applications continuously and encourage you to apply early, as the position may be filled before the closing date. Please note that, due to GDPR regulations, we cannot accept applications via email. This is a full-time position (100%) based at our headquarters in Frösundavik, Solna, Stockholm. We value in-person collaboration, although flexible working arrangements may be available depending on team and business needs. As this role contributes to security-classified systems and business-critical capabilities, a background check will be conducted as part of the final stage of the recruitment process.