Data Scientist • Junior

Who you are

You hold a Bachelor's degree in Computer Science, Data Engineering, or a related field and have 2-4 years of experience in MLOps, ML Engineering, or DevOps for ML. You are proficient in Spark and MLflow, with strong experience in Databricks and Azure ML. Your solid Python and SQL skills are complemented by your knowledge of containers, particularly Docker and Kubernetes. You are familiar with CI/CD concepts and tools like Azure DevOps or GitHub Actions, which allows you to effectively contribute to the deployment processes.

Desirable

Familiarity with Kubernetes (AKS), Terraform, and model observability practices would be a plus. Experience deploying Power BI dashboards that consume predictions is also desirable, as it would enhance your ability to integrate data insights into business systems.

What you'll do

In this role, you will design and develop MLOps pipelines for model training, deployment, and retraining, ensuring that models are efficiently integrated into business systems. You will containerize models using Docker and deploy them via Azure Databricks or AKS, which will require you to implement CI/CD workflows with MLflow and GitHub Actions. Monitoring model performance and data drift using Azure-native tools will be a key responsibility, as will collaborating with Data Scientists and Engineers to optimize ML deployment processes. You will document and standardize these processes to ensure consistency and efficiency across the team.

What we offer

Wizeline provides a high-impact environment where you can grow your skills and contribute to innovative projects. We are committed to your professional development and foster a flexible and collaborative culture. As part of our vibrant community, you will have access to global opportunities that allow you to make a significant impact in the tech industry. Specific benefits are determined by the employment type and location, ensuring that you receive the support you need to thrive in your role.