The focus of Digital Hangar is the improvement of passenger experience throughout the whole journey. The projects that are in focus are Lufthansa Group (Lufthansa, SWISS, Austrian and Brussels) main web portals, mobile apps (iOS and Android) and B2B platforms.
Who are we?
Lufthansa Systems Poland is acclaimed and widely recognized provider of superior services and IT solutions for the aeronautical, logistic and financial industries. As a part of the Lufthansa Group we implement technological solutions and dedicated services.
Who are you?
- At least 2 years of commercial experience in Data Science or Machine Learning,
- Strong skills in Python, SQL,
- Hands-on experience in data exploration, analysis and feature engineering,
- Practical experience with machine learning frameworks such as Scikit-learn,
- Familiarity with PySpark or distributed computing concepts,
- Experience with version control and collaboration with others on the same code base,
- Experience with the Azure ecosystem (Azure ML Studio, Azure Data Factory, Delta Lake) or other cloud-based platforms,
- Exposure to tools like Databricks and Notebooks for collaborative data work,
- Understanding of the end-to-end ML lifecycle: from data ingestion, through model training and evaluation, to deployment in production and monitoring (incl. post-development, e.g. drift detection, retraining incl. A/B testing models against each other),
- A proactive mindset and an ability to think critically and strategically beyond the task at hand,
- Very good command of English (spoken and written),
- Willingness to travel occasionally.
Nice to have:
- Experience working in Scrum, SAFe or other agile methodologies,
- Exposure to MLOps, model monitoring, or CI/CD pipelines for ML,
- Working in international and interdisciplinary teams,
- Familiarity with software engineering best practices in ML projects,
- Solid understanding of statistics — and the ability to choose and apply the right methods when exploring or validating data,
- Experience working with recommendation systems or within an online marketing environment,
- Familiar with the concept of data products, ideally based on previous experience,
- Previously worked with customer data and familiar with GDPR policies.
Why do we need you?
In this role, you will be responsible for:
- We’re looking for a Data Scientist who’s passionate about turning complex data into actionable insights — and isn’t afraid to dive into the world of data pipelines when needed.
- If you're skilled in building and validating machine learning models, exploring large datasets, and want to collaborate with engineers and analysts on real-world problems — we’d love to meet you.
- In this role, you’ll dive into large, sometimes messy datasets to uncover insights that drive real value for the product and the business. You’ll collaborate closely with data engineers to build reliable, scalable machine learning pipelines, and experiment with new models to see what actually works in production. You’ll also take ownership of the full MLOps lifecycle — from training to deployment and monitoring — all within Azure ML.
- Beyond the technical work, you’ll work with internal stakeholders to turn their ideas and challenges into concrete, data-driven solutions. And when it’s time to share results, you’ll be able to explain your findings clearly and effectively — whether you’re speaking to product managers, analysts, or anyone else who needs to understand the impact.
- If you’re excited about applying your ML skills in a practical, business-oriented context — and you enjoy jumping between data, code, and communication — we’d love to hear from you.
What do we offer?
- An international working environment, atmosphere that stimulates development,
- Individual career path,
- Lufthansa Group membership benefits,
- Salary in the range of:
- Standard position 8 000 - 16 000 zł gross (contract of employment)
- Senior position 10 000 - 21 000 zł gross (contract of employment)
- Support for your passion for sports within the local activity group and co-financing Multisport cards,
- Private medical care for employees and their family members.
