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Job Summary: We are seeking a Machine Learning specialist to develop predictive models for supplier delivery performance, optimizing the supply chain and communication. Key Highlights: 1. Development of predictive Machine Learning models 2. Improvement of accuracy and responsiveness of the Material Requirements process 3. Working with industrial/operational contextual data **Company Description** The Bosch Group employs more than 400,000 people worldwide across 60 countries, and we are proud to impact people's lives and work toward a more sustainable future. Bosch Car Multimedia, S.A. belongs to the Automotive Electronics division and focuses on making vehicles our third living space. Approximately 3.600 employees are committed to developing and producing high-quality technology that drives mobility transformation globally. The company’s success lies in its highly specialized and innovative team and technological know-how, positioning Bosch as the leading automotive supplier. At Bosch, we shape the future by creating high-quality products and services that inspire and improve people’s lives. Our promise to employees is strong: we grow together, enjoy what we do, and inspire one another. Join us and experience the difference in mindsets, cultures, generations, identities, and perspectives. Everyone should bring their authenticity and collaborate respectfully. Bosch is an employer that values diversity and equal opportunity. We welcome applications from persons with disabilities and can provide reasonable accommodations throughout the recruitment process and during job performance. Inclusion of all and ensuring equal opportunity enable us to reach our true potential. **Job Description** **Your contribution to something greater**: * Develop a Machine Learning model to predict supplier and transportation delivery performance (on-time delivery/delays) for individual components across all factories/plants; * The ultimate goal is to improve the accuracy and responsiveness of the Material Requirements process by integrating delivery reliability forecasts and supporting more proactive communication with suppliers; * Identify data sources and map the “order-shipment-delivery” flow across all plants; * Build a historical dataset and define labels/targets (OTD, delay, risk classes); * Develop a data preparation pipeline and feature engineering per supplier/component/plant/transportation; * Enhance supplier communication based on predictions (targeted and anticipatory follow-ups); * Train and compare models to select the best trade-off between performance and interpretability; * Evaluate robustness per plant/supplier (operational fairness) and across time windows (temporal validation); * Generate actionable metrics: delay probability, predicted delay days, and risk ranking per order/PO; * Create operational outputs (API, dashboard, or batch file) with predictions and explanations; * Define model monitoring and maintenance (drift, data quality, re-training). **Qualifications** **What sets you apart**: * Skills in Machine Learning and data analysis (supervised models, validation, metrics); * Experience with Python and data libraries (pandas, scikit-learn), and version control (Git). Knowledge of databases/SQL and data pipelines (ETL/ELT); * Ability to work with industrial/operational contextual data (procurement, logistics, supply chain); * Clear communication skills to translate results into actions (planning, procurement, and logistics teams); **Additional Information** **Work \#LikeABosch includes**: Hybrid work model Sharing experiences with colleagues worldwide * ️ On-site medical clinic (psychology and general practice) & social services office Training opportunities (e.g., technical training, foreign language courses) Continuous professional development Access to significant discounts through Bosch partnerships and products ️ Sports and health-related activities Excellent access to public transport Free transportation from Porto ️ Free parking lot ️ Canteen **Success stories don’t happen by chance...** **Make them happen.** **Apply now!**
