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 youre 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.
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.
responsibilities :
Analysis and exploration of large datasets, as well as feature engineering,
Designing, training, and evaluating ML models using Scikit-learn and other frameworks,
Building and maintaining ML pipelines based on Azure ML Studio, Azure Data Factory, and Delta Lake,
Collaborating with the Data Engineering team on designing and optimizing data processing workflows,
Deploying models to production environments and monitoring them (incl. drift detection, retraining, A/B testing),
Working with tools for analysis and code collaboration (Git, Databricks, Notebooks),
Ensuring solution quality and scalability by applying MLOps best practices,
Close collaboration with the product team to identify and implement solutions that generate business value.
requirements-expected :
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.
offered :
An international working environment, atmosphere that stimulates development.
Individual career path.
Lufthansa Group membership benefits,
Flexible working time and place adjusted to employee’s needs. Possibility of starting your workday between 07:00 and 11:00.
Workplace adjusted to employees needs.
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,
Life insurance,
No dress code,
Corporate products and services at discounted prices,
Parking space for employees,
Holiday funds,
Sharing the costs of holidays for kid
benefits :
sharing the costs of sports activities
private medical care
life insurance
flexible working time
fruits
corporate products and services at discounted prices
