Join our dynamic team at the Data Science where you will contribute to delivering high-quality data solutions that drive critical business decisions in supply chain and marketing. Collaborate with a diverse group of data scientists, analysts, and engineers to build, scale, and operate our cutting-edge machine learning and analytics platform in a cloud-based environment.

ML Ops with Azure/Databricks

Your responsibilities

• Optimize, standardize, and implement scalable data science and machine learning solutions in Azure cloud environments.
• Participate in the complete lifecycle of data science projects using DevOps methodologies, including code, experiment, and model management, as well as CI/CD practices.
• Write well-designed, testable, and efficient code.
• Collaborate with engineering teams to enhance data consumption and model deployment processes.
• Design and oversee monitoring, troubleshooting, debugging, and incident management for our ML pipelines.
• Act as a trusted advisor and advocate for the team and stakeholders on ML Ops aspects, including scaling, throughput, infrastructure, and deployment strategies

Our requirements

• Minimum of 2 years of professional experience in ML Ops or ML engineering, with a focus on productionizing and scaling ML models.
• Strong familiarity with Azure cloud environment and Databricks, including setup and maintenance for ML applications.
• Advanced proficiency in Python and SQL, with experience in version control systems (Git) for building ML and data pipelines.
• Proven experience with software development best practices, including testing, continuous integration, and DevOps tools.
• Solid understanding of the data science lifecycle and the operational methodologies of data scientists.
• Familiarity with agile software development methodologies (SCRUM, Kanban).
• Attention to code clarity, ease of development, and correctness of implementations.
• Business-ready proficiency in English, both written and spoken.
• Experience in productionizing ML tools with user interfaces (e.g., Dash, Shiny R, Streamlit).
• Familiarity with Azure cloud resource setup for data solution purposes.
• Strong communication skills to explain complex ML Ops topics to audiences with varying technical backgrounds.
• Experience in coaching or mentoring junior team members in your areas of expertise.