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Seagate TechnologyEngineering Manager – Factory Sustainable Engineering
Lead Hardware engineer team at Seagate's Factory Sustainable Engineering department. Focus on automation improvement, technology enhancement, and mentorship in engineering.
About the role
- Recruit, hire, and develop a high-performing team of AI/ML and Data Science Engineers, fostering technical excellence and continuous learning.
- Architect and drive the roadmap for building AI-native infrastructure, platforms, and processes tailored for the manufacturing environment.
- Guide the end-to-end lifecycle of machine learning projects: data acquisition, feature engineering, model training, validation, and deployment of predictive and anomaly detection systems in production.
- Implement and evangelize MLOps best practices to ensure scalability, reliability, and continuous improvement of ML systems.
- Collaborate with manufacturing operations leaders, plant floor engineers, and IT partners to identify high-value use cases and translate business needs into AI-driven solutions.
- Set and maintain high standards for code quality, system performance, and scientific rigor across data science and machine learning projects.
- Lead project execution using agile methodologies, ensuring on-time delivery, clear stakeholder communication, and effective priority management in a dynamic environment.
Requirements
- A Bachelor’s degree in Computer Science, Statistics, Engineering, or a related quantitative field.
- 3+ years of formal leadership experience, managing and mentoring technical teams in a data science or software engineering capacity.
- 5+ years of hands-on experience in data science and machine learning, with deep expertise in predictive modeling, classification, and unsupervised learning techniques (like anomaly detection).
- Proven experience building and deploying machine learning models into production environments.
- Strong proficiency in Python and common data science/ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Pandas).
- Experience with cloud computing platforms (e.g., GCP, Azure, or AWS) and their associated AI/ML services.
- Excellent problem-solving skills and the ability to navigate complex, ambiguous challenges.
- Fluency in both English and Spanish, with strong verbal and written communication skills.
- (Preferred) A Master’s degree or PhD in a relevant technical field.
- (Preferred) Experience in a manufacturing, industrial automation, or IoT environment.
- (Preferred) Deep knowledge of MLOps principles and experience with related tools (e.g., Kubeflow, MLflow, Seldon Core).
- (Preferred) Experience with real-time data streaming technologies (e.g., Kafka, Flink) and time-series analysis.
- (Preferred) Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).
- (Preferred) A strong portfolio of deployed AI/ML projects that have delivered measurable business value.
Job title
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