Data Science Specialist I collaborating with Data Science team to build data solutions for user experience and scalable business metrics. Focused on MLOps, Cloud environments, and model observability.
Responsibilities
Build and maintain end-to-end training pipelines, versioning, testing, and deployment of ML models
Implement MLOps practices for automation, monitoring, and maintenance of models in production
Manage environments, containers, GPUs, scalability, and resource orchestration
Work in partnership with AI Scientists to turn experiments into production-ready solutions
Ensure model observability by monitoring drift, performance, cost, logs, and metrics
Create experimentation infrastructure and standardize AI development workflows
Support security, governance, and best practices in model lifecycle management
Requirements
Experience with ML/MLOps pipelines, CI/CD (GitHub Actions), and tools such as Kubeflow, MLflow, Airflow, and AWS SageMaker
Knowledge of containers (Docker), Kubernetes, APIs, and Python
Experience with Cloud environments (AWS, GCP, or Azure)
Strong understanding of Machine Learning models
Plus: Experience with large-scale data modeling and handling
Experience with real-time / near-real-time model processing and deployment
Benefits
Market-competitive salary
Health insurance
Dental plan
Private pension plan
Flexible benefits that adapt to your needs
Meal allowance
Financial counseling
Perks club
Home office allowance
Mobility allowance
Childcare assistance
Life insurance
Extended leave
Incentives for sports, quality of life, and wellbeing
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