About the role
Manager, Smart Manufacturing & Artificial Intelligence Global Operations Improvement role based on the published job description. Key responsibilities and requirements were extracted directly from the posting for quick review.
IDMOnsiteSmart MFG/AI
Key Responsibilities
- As the Data Science Team Manager within the SMAI (Smart Manufacturing & AI) department, you will lead a team of data scientists to develop and deploy AI-driven solutions that support global operational excellence.
- Play a key role in driving SMAI's strategic initiatives by using advanced analytics, cloud technologies, and across function collaboration.
- Team Leadership & Development Lead, mentor, and grow a team of data scientists focused on delivering scalable AI solutions aligned with SMAI's global strategy.
- Foster a culture of innovation, accountability, and continuous learning.
- Strategic Execution & Partner Engagement Translate SMAI department strategy into actionable data science initiatives that support business goals across manufacturing, supply chain, and operations.
- Collaborate with global partners to identify high-impact opportunities and ensure alignment with organizational priorities.
Requirements
- Educational Background Bachelor's or Master's degree or equivalent experience in Data Science, Computer Science, Statistics, Engineering, or a related field Japanese & English communication skill Professional Experience 5+ years of experience in data science or analytics, with at least 2 years in a leadership or managerial role.
- Proven track record of delivering AI-related solutions in manufacturing, operations, or supply chain domains.
- Technical Skills Strong proficiency in SQL , Python, and data modeling techniques.
- Hands-on experience with Google Cloud Platform (GCP) services such as BigQuery, Vertex AI, and Cloud Functions.
- Familiarity with machine learning frameworks (e.g., TensorFlow, scikit-learn) and MLOps practices.
- Strategic & Capabilities across Functions Ability to translate SMAI department strategy into actionable data science initiatives.