Data Scientist

Kapsch is one of Austria's most successful global technology companies. With its comprehensive ITS (Intelligent Transportation Systems) portfolio, Kapsch is actively addressing the challenges of the present and the future with intelligent mobility solutions in a wide range of application areas. As a family-owned company founded in 1892 and headquartered in Vienna, Kapsch can look back on 130 years of experience with the future. To learn more about our solutions and innovations, visit our website here . We are looking for a Data Scientist with experience developing machine learning solutions and analytical models, from experimentation to validated production-ready models. You will work closely with the Product Owner, Data Engineers, MLOps Engineers, and Data Analysts to transform business challenges into scalable AI solutions and data products. What you’ll do Design, develop, and validate machine learning models through iterative experimentation and PoC cycles. Work directly with customers and stakeholders to understand business problems, define use cases, KPIs, and success criteria. Develop forecasting, predictive, and analytical models, with strong focus on time series use cases. Collaborate closely with Product Owners, Data Engineers, Data Analysts, and MLOps Engineers to deliver AI and ML solutions efficiently. Perform feature engineering, model validation, and model performance analysis. Build demo-ready PoCs and visualizations to communicate results and insights to stakeholders. Track experiments, metrics, and model evolution using MLflow. Work with analytical datasets and contribute to the definition of datasets and features for ML use cases. Contribute to technical standards, best practices, and AI/ML solution design. What we’re looking for 4+ years of experience as a Data Scientist, ML Engineer, or similar role focused on analytical and ML solutions. Degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or a related field. Strong background in machine learning. Experience managing ML experimentation cycles, model lifecycle, and production model maintenance, using tools such as MLflow or similar. Proficiency in Python and experience working with SQL. Experience working with analytical databases and large datasets. Familiarity with orchestration tools such as Apache Airflow and notebook-based experimentation workflows. Strong English communication skills, both written and spoken. Analytical mindset, fast learner, collaborative attitude, and customer-oriented approach. Experience working in Agile environments. Our offer Permanent role. Hybrid working model (3 days of remote work/week). Flexible working hours. 30 business days of annual leave. Flexible remuneration plan.