Data Scientist - ML Engineering

Tasks

• Build trust and credibility
• Deploy ML at enterprise scale
• Deploy high-scale ML systems in hybrid/multi-cloud environments
• Identify workflow streamlining opportunities
• Lead ML platform or DevOps teams
• Lead complex client conversations
• Leverage AI tools for workflow optimization
• Monitor and audit ML systems
• Translate technical topics into business impact
• Utilize AutoML and H2O
• Work with feature stores and feature engineering

Perks/Benefits

• Flexible culture
• Global opportunities
• High-impact environment
• Professional development
• Total rewards
• Vibrant community

Skills/Tech-stack

AI Tooling | Automl | Azure Databricks | Cloud infrastructure | Docker | Feature Engineering | H2O | Kubernetes | ML deployment | MLflow | Monitoring | Multi-cloud | Multi-cloud infrastructure | Spark