• Build, validate, and deploy statistical and AI models to solve complex business problems. • Design and refine predictive features, ensuring data quality and model interpretability. • Leverage Azure Machine Learning and Microsoft’s suite of data services to scale model training and deployment. • Apply statistical rigor to analyze data trends and communicate findings to non-technical stakeholders.

• Analyzes business requirements and determines a suitable solution autonomously, evaluating if an ML-based solution is feasible • Good understanding of business requirements • Develops and fine-tunes models through reproducible experiments • Builds ML solutions incorporating software engineering quality standards (SDLC) and data engineering best practices • Participates in the technical design of features with guidance • Understands and optimizes and monitors model performances • Prioritizes tasks with autonomy based on requirements and proper context.

• Lead the data science lifecycle, from discovery and exploration to delivery and customer hand-off • Translate ambiguous business problems into clear problem statements and deliver working, production-ready solutions • Partner with customers and internal stakeholders to co-design use cases, define success criteria, and ensure models are adopted and successful in production • Build quick, iterative prototypes to test ideas, and work cross-functionally to deploy validated solutions into production environments • Own and improve pipelines for data integration, validation, and monitoring, ensuring long-term stability and performance • Analyze large-scale structured and unstructured datasets to extract insights and power model development • Apply machine learning, NLP, graph analytics, and other advanced techniques to solve challenging problems such as semantic search, or anomaly detection • Create and maintain internal data science tooling to streamline experimentation, testing, and delivery • Support internal teams and customers with issue resolution, ensuring data science solutions continue to deliver value post-deployment • Follow and advocate for best practices in MLOps, DataOps, and clean code to ensure reproducibility, maintainability, and scalability

• Einblicke in die Welt der Datenanalyse und des maschinellen Lernens erhalten • Unterstützung bei administrativen und organisatorischen Aufgaben • Verantwortung für eigene kleine Projekte, die als Grundlage für die Abschlussarbeit dienen können • Aktive Teilnahme an Data Science-Projekten und Nutzung verschiedener Methoden des maschinellen Lernens • Aufbereitung, Analyse von Daten sowie Erstellung von Visualisierungen und Berichten