Hybrid Data Science Specialist I – ML Engineering

Posted 2 weeks ago

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About the role

  • 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
  • Payroll-deductible loans at reduced rates

Job title

Data Science Specialist I – ML Engineering

Job type

Experience level

Mid levelSenior

Salary

Not specified

Degree requirement

No Education Requirement

Location requirements

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