Remote Data Scientist - ML/AI & MLOps

Quik Hire Staffing; Ascoli Piceno; Remoto; EUR 70.000 - 110.000; Tempo pieno

Descrizione del lavoro

Quik Hire Staffing is seeking a Data Scientist (ML) to join a client project on a full-time basis. The role focuses on developing and deploying machine learning models to boost predictive accuracy for client datasets.

You will collaborate with cross-functional teams to identify data-driven solutions and automate workflows. Responsibilities include designing, training, and optimizing models, preprocessing large datasets, and deploying MLOps pipelines with Python and notebooks.

Competenze

• Strong proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch).
• Experience with SQL for data extraction and analysis.
• Solid background in statistical modeling and experimental design.
• Familiarity with cloud platforms (AWS, GCP, Azure) for deployment.
• Ability to work independently in a remote environment.

Mansioni

• Design, train, and optimize machine learning models for classification, regression, and clustering tasks.
• Preprocess and analyze large-scale structured and unstructured datasets to extract actionable insights.
• Implement and maintain MLOps pipelines for model deployment, monitoring, and retraining.
• Develop and document reproducible experiments using Python, Jupyter notebooks, and version control systems.
• Present findings and recommendations to stakeholders through clear visualizations and technical reports.

Conoscenze

Python

Scikit-learn

TensorFlow

PyTorch

SQL

Statistical modeling

Experimental design

Cloud platforms (AWS, GCP, Azure)

Remote work ability

Strumenti

Jupyter notebooks

Version control

Descrizione del lavoro

Quik Hire Staffing is seeking a Data Scientist (ML) to join a client project on a full-time basis. The role focuses on developing and deploying machine learning models to boost predictive accuracy for client datasets.

You will collaborate with cross-functional teams to identify data-driven solutions and automate workflows. Responsibilities include designing, training, and optimizing models, preprocessing large datasets, and deploying MLOps pipelines with Python and notebooks.