Job Informationen

Location: Andelfingen Workload: Full-time Your tasks: Design and deploy machine learning models for prediction and decision-making on domain-specific operational data Advance and extend scheduling and resource optimisation, including multi-objective optimisation, constraint handling, and stable re-planning Build end-to-end models that learn from real operational data and improve planning accuracy over time Own the full ML lifecycle: data analysis, feature engineering, model development, evaluation, and monitoring Translate business problems into formal models and measurable outcomes Examples of What You’ll Build Capacity planning algorithms that account for skills, time buffers, cool-down periods, and space constraints Intelligent job-to-talent matching with dynamic weighting for in-progress vs. new projects Automated project creation from structured and unstructured operational inputs (e.g. PDFs, free text) Your profile: Master’s or PhD in Computer Science, Mathematics, Physics or a related field Several years of experience in machine learning, particularly with tabular data and time series Strong knowledge of at least one deep learning framework (PyTorch is preferred) Experience with mathematical optimisation and/or constraint programming (e.g. OR-Tools, CP-SAT, Gurobi) Proficient in Python and the data science ecosystem (e.g. pandas, scikit-learn) Experience with ML experiment tracking and model deployment (e.g. MLflow, Docker, Kubernetes) A self-driven, solution-oriented mindset: you don’t just build models, you understand the business problem behind them Fuent in English

Benötigte Skills

• Englisch
• Mathematik
• Physik
• Monitoring
• Python
• Machine Learning
• Master