About the company

Since 1979, Seagate has been creating precision-engineered data storage technologies that deliver superior capacity, speed, safety, and performance. We are a diverse workforce of 40,000 creative, hard-working, passionate people all over the world—Silicon Valley, the Southeast Asia, India, China, the United Kingdom, and beyond. At Seagate, we know that data is always in motion, alive, connected—and we harness it in order to maximize human potential.

About our group:

The Data Science Group works to help our internal customers turn the data they have into the information they need to develop and deliver industry-leading storage solutions. 

About the role - you will:

• Understand business requirements to build reliable data infrastructure using big data technologies  
• Maintain scalable machine learning solutions in production and take end-to-end ownership of the ML systems and solutions from data pipelines, model training, optimization, verification, deployment to real-time prediction engines 
• Build AI/ML models: apply ML algorithms and libraries, train and retrain ML systems and models, tune and analyze AI/ML algorithms and extract actionable insights 
• Play a key role in MLOps: follow machine learning model management, lifecycle best practices, and MLOps for all AI/ML models created 
• Design ML systems, research and implement ML algorithms and tools 
• Identify, design and implement internal process improvements and tools to automate data processing and ensure data integrity while meeting data security standards 
• Architect and create data views from big data store to feed into analysis engines, visualization engines etc. 
• Work with data scientist and business analytics team to assist in data ingestion and data-related technical issues 
• Focus on Seagate advanced analytics projects related to Machine Learning (ML) 
• Develop data mining algorithms, perform data wrangling and exploration 

About you:

• Data science/Machine Learning evangelist and an assessor in promoting new ways of diving into complex issues while assessing current practices and identifying areas where training or knowledge would help 
• Strong interpersonal and communication skills (written and verbal) 
• Self-starter with the ability to drive initiatives 
• Fast and independent learner  
• Good team player with a collaborative attitude and a global mindset to work well with native and non-native English speakers 

Your experience includes:

• Experience in Python (and basic ML libraries), Java, and SQL 
• Knowledge of ML frameworks and hands-on deployment experience 
• Proficient in Git version control and experience working in an agile environment. 
• Strong foundation in data modeling, data structure, CI/CD, MLOps, software engineering, and system architecture design principles 
• Build production-ready ML models 
• Experience in Kubernetes/Docker and Microservices 
• Ability to deploy data science solutions in cloud and edge analytics infrastructure, and AWS 
• Solid technical knowledge and hands-on skills in machine learning engineering, deep learning, statistical algorithms, data mining, and data structures 
• Ability to participate in conference calls outside of regular local work hours