Microsoft Data Scientist II 

Taiwan, Taoyuan City 

988842497

Required Qualification:

• 4+ years of experience in machine learning, MLOps/AIOPs, or software engineering roles.
• Proven track record of deploying large-scale machine learning systems in production.
• Strong experience with cloud platforms (Azure preferred) and infrastructure as code (e.g., Terraform, ARM templates).
• Advanced knowledge of MLOps/AIOPs practices, including pipeline automation, monitoring, and orchestration.
• Experience optimizing ML models for performance and scalability in production environments.
• Demonstrated ability to lead initiatives, mentor junior team members, and influence cross-functional teams.
• Solid understanding of security and compliance frameworks relevant to ML operations.
• Hands-on experience in building and deploying ML models in a cloud environment (preferably Azure).
• Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch).
• Experience with containerization (Docker, Kubernetes) and microservices architecture.
• Strong knowledge of CI/CD tools and workflows (Azure DevOps, GitHub Actions).
• Basic understanding of model monitoring, retraining, and model governance practices.

Preferred Qualification:

• Experience with Azure Machine Learning, Azure Fabric, Synapse, or similar platforms.
• Strong understanding of data versioning, governance, and reproducibility in ML workflows.
• Knowledge of responsible AI practices, including fairness, transparency, and bias mitigation.
• Strong communication skills and the ability to work in a fast-paced, collaborative environment.

Model Development & Deployment:

• Collaborate with data scientists and engineers to design, build, and deploy machine learning models at scale.
• Develop and maintain MLOps/AIOPs pipelines to automate the end-to-end lifecycle of machine learning models (from development to deployment, monitoring, and retraining).
• Work on the integration of models into production systems while ensuring scalability, security, and performance.

Model Operationalization:

• Implement CI/CD pipelines for ML models, ensuring smooth deployments with minimal downtime.
• Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew.
• Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations.

Optimization & Scalability:

• Optimize machine learning models and pipelines for performance and cost efficiency (compute, storage).
• Manage infrastructure for ML workloads using cloud-native tools (Azure, Kubernetes, Docker) or other container orchestration platforms.

Collaboration & Communication:

• Partner with cross-functional teams, including Data Engineering, Product Management, and other Engineering teams to build cohesive solutions.
• Provide technical guidance to junior engineers and drive best practices for MLOps/AIOPS within the team.

Security & Compliance:

• Work on securing models, data pipelines, and infrastructure in compliance with Microsoft's security standards.
• Ensure that the entire ML lifecycle adheres to privacy and compliance requirements (e.g., GDPR, CCPA).