Internship - Data Scientist

The AI/Data Science Intern will support both (1) sustenance and continuous improvement of existing production AI applications and (2) development of new AI use cases across multiple problem types (e.g., computer vision, predictive modeling, anomaly detection, optimization, and GenAI where applicable).  Your role Key responsibilities in your new role Production Model Monitoring & Remediation: Monitor and sustain production AI model performance, investigate deviations, and implement corrective actions. Use Case Exploration & Prototyping: Explore and assess feasibility, and prototype new AI use cases aligned with business needs and KPIs. Data Pipeline Enhancement: Enhance data pipelines and preprocessing to improve data quality, robustness, and repeatability. Model Evaluation & Validation: Evaluate, benchmark, and validate models against KPIs, documenting results, risks, and limitations. Cross-Functional Collaboration & Enablement: Collaborate with cross-functional teams and drive knowledge sharing through documentation and demos. Deployment, Scaling & Continuous Improvement: Support continuous improvement, deployment readiness, and scaling of AI solutions across processes and sites. Internship Learning Outcomes: Model Monitoring & Maintenance: Gain hands-on experience in monitoring, maintaining, fine-tuning, and optimizing deployed AI models.   Data Pipeline Improvement: Strengthen skills in improving and troubleshooting data pipelines to support reliable model performance. Business KPI Evaluation: Learn to evaluate models against business KPIs to assess real-world impact and effectiveness. Deployment & Scaling Exposure: Contribute to and gain exposure to model deployment and scaling efforts across multiple processes and sites. Your profile Qualifications and skills to help you succeed  Education: Bachelors/ Masters in Data Science or a related field. ML Fundamentals: Solid foundation in machine learning fundamentals (training/evaluation, overfitting, cross-validation). Programming & SQL: Proficiency in Python and basic SQL for data extraction and analysis. Version Control & Reproducibility: Familiarity with Git and reproducible development workflows. MLOps & Containerization: Awareness of containerization and orchestration basics (Docker/Kubernetes). GenAI & Agentic AI Exposure: Understanding of GenAI/LLMs (prompt engineering, embeddings, RAG) and related tooling; basic agentic AI concepts. Learning Mindset & Collaboration: Strong learning mindset and ownership: proactive, detail-oriented, able to communicate clearly and collaborate across teams. #WeAreIn for driving decarbonization and digitalization. As a global leader in semiconductor solutions in power systems and IoT, Infineon enables game-changing solutions for green and efficient energy, clean and safe mobility, as well as smart and secure IoT. Together, we drive innovation and customer success, while caring for our people and empowering them to reach ambitious goals. Be a part of making life easier, safer and greener. Are you in? We are on a journey to create the best Infineon for everyone. We strive to create the best Infineon for everyone by embracing diversity and inclusion. We welcome applicants for who they are and offer a workplace built on trust, openness, respect and equal opportunity.  Our hiring decisions are based on skills and experience. Even if you don’t meet every requirement, we encourage you to apply. Please let your recruiter know if you need any accommodations during the interview process. Learn more about our various contact channels and about Diversity & Inclusion at Infineon. Hillary Woo https://www.linkedin.com/in/hillary-woo/