About the role
Industrial Data Science & AI Manager role based on the published job description. Key responsibilities and requirements were extracted directly from the posting for quick review.
Aerospace & DefenseOnsite
Key Responsibilities
- Team and data science portfolio management: Responsible for the Roadmap of the data science and manage data science ROI and Portfolio, develop requirements and ensure value and benefit the shopfloor operation, guide data science engineers to develop and review their work as well to grow their skills and experience.
- Project Planning: Develop comprehensive project plans, defining scope, objectives, deliverables, timelines, resource allocation, and budget estimates for industrial data science projects.
- Stakeholder Engagement: Collaborate with stakeholders to understand business needs, gathering requirement, operational challenges, and opportunities for leveraging data science to drive value.
- Technical capability on Data Science: Work and guide data engineers and domain experts to identify relevant data sources, extract, clean, and preprocess data for analysis and application and modeling, Mentor and develop data science and data engineering team members, fostering technical excellence, knowledge sharing, and continuous learning within the team.
- Data Analysis and Modeling: Lead data exploration, statistical analysis, and machine learning model development to uncover insights, patterns, and trends in industrial data.
- Model Deployment: Oversee the deployment of data science models into production environments, ensuring scalability, reliability, and integration with existing systems. Deploy standards defined and contribute to their improvements.
Requirements
- Bachelor's degree in computer science, data science, industrial engineering, or a related field
- Advanced degree or relevant certifications preferred.
- At least 8 years proven experience in project management, specifically in leading data science or analytics projects in industrial settings.
- Experience managing small team and data science portfolio
- Experiences on requirement gathering, scoping, data mapping and data driven improvement, digital transformation projects to deliver business objectives are plus
- Strong technical proficiency in data science tools and techniques, including architecting, statistical analysis, machine learning, predictive modeling, and data visualization.