You will be part of a team dedicated to delivering efficient and high-quality data solutions. You will collaborate with data scientists, data analysts and engineers to build, scale and operate our machine learning & analytics platform. Your skill, competence and creativity will directly support and impact critical business decisions in areas of supply chain and marketing.

• Optimize, standardize and implement data science and machine learning solutions at scale and in cloud-based environments (Azure)
• Participate in the end-to-end lifecycle of data science projects through the use of DevOps, code, experiment and model management, CI/CD and further industry best practices
• Write well-designed, testable, efficient code
• Work closely with engineering to continuously improve the way we consume data and deploy models in production
• Design and lead on monitoring, troubleshooting, debugging and incident management for our ML pipelines
• Be a trusted advisor and evangelist to the team and stakeholders on various aspects of ML Ops, from scaling and throughput, to infrastructure and deployment strategies

• 2+ years of professional experience in ML Ops or ML engineering, particularly in productionizing and scaling ML models
• Broad familiarity with Azure cloud environment and Databricks, including its setup and maintenance as an ML platform
• Advanced proficiency with Python and SQL in version control systems (git) plus their use in building ML & data pipelines
• Proven experience with software development best practices including testing, continuous integration, and DevOps tools
• Good understanding of data science lifecycle and the way data scientists work to deliver value
• Familiarity with agile software development lifecycle (SCRUM, Kanban, etc.)
• Attention to clarity of code, ease of development, and correctness of implementations
• Business-ready command of English, written and spoken

• Experience in productionizing ML tools involving a user interface, e.g. Dash, Shiny R, Streamlit
• Experience in Azure cloud resource setup for purposes of data solutions
• Great communication skills to guide audiences of a broad technical knowledge range through complex ML Ops topics  
• Practice in coaching / mentoring younger team members in your expertise areas