Remote Data Scientist - AI/ML & MLOps

Quik Hire Staffing

Italia

Remote

EUR 45,000 - 65,000

Full time

2 days ago
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Job summary

Quik Hire Staffing is seeking a Data Scientist (ML) on a full-time basis to develop and deploy machine learning models that improve predictive accuracy on client datasets. You will collaborate with cross-functional teams to automate workflows and deliver data-driven solutions.

The role emphasizes Python, Jupyter notebooks, and experience with ML libraries, SQL, and cloud deployment. A remote-friendly environment with minimal supervision is offered, with exposure to AI-driven solutions in a

Qualifications

  • Proficiency in Python and libraries such as scikit-learn, TensorFlow or PyTorch.
  • Experience with SQL for data extraction, transformation and analysis.
  • Strong background in statistical modeling and experimental design.
  • Familiarity with cloud platforms for model deployment (AWS/GCP/Azure).
  • Ability to work independently in a remote environment with minimal supervision.

Responsibilities

  • Design, train, and optimize ML models for classification, regression, and clustering.
  • Preprocess and analyze large-scale structured and unstructured datasets.
  • Implement and maintain MLOps pipelines for deployment, monitoring and retraining.
  • Develop reproducible experiments using Python, Jupyter notebooks, and version control.
  • Present findings to stakeholders with clear visualizations and reports.

Skills

Python
scikit-learn
TensorFlow
PyTorch
SQL
Statistical modeling
Experimental design
AWS
GCP
Azure

Tools

Jupyter notebooks
Version control

Job description

Quik Hire Staffing is seeking a Data Scientist (ML) on a full-time basis to develop and deploy machine learning models that improve predictive accuracy on client datasets. You will collaborate with cross-functional teams to automate workflows and deliver data-driven solutions.

The role emphasizes Python, Jupyter notebooks, and experience with ML libraries, SQL, and cloud deployment. A remote-friendly environment with minimal supervision is offered, with exposure to AI-driven solutions in a

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