About Us

IWSR is the global authority on beverage alcohol data and intelligence

For over 50 years, IWSR has been trusted by the leaders of global beverage alcohol businesses as an integral part of their strategic planning and decision-making processes. We uniquely combine our proprietary longitudinal market data, consumer insights and AI-enhanced data science, with valuable on-the-ground human intelligence in 160 markets worldwide, to decipher what is really happening in the global beverage alcohol market. With access to our data, clients from across the drinks industry, including multinational spirits, beer, and wine businesses; packaging and ingredient manufacturers; distributors; and financial institutions, plan their strategies and future investment with a reliable, consistent and complete understanding of the global landscape.

Role Overview

We’re seeking an ML Ops Engineer to drive the adoption of robust, scalable, and consistent data science and data analysis workflows across IWSR. You\'ll work at the intersection of data engineering, data science, and DevOps — helping us move to a more structured and mature approach using Databricks on AWS.

Your work will empower our analysts and data scientists to be more productive and consistent, by setting up reusable tools, automated deployment pipelines, and standardized workflows. This is a hands-on role with a focus on enablement, automation, and best practices.

Key Responsibilities

• Set up and manage Databricks infrastructure, including job scheduling, cluster configurations, and workspace organization.
• Build CI/CD pipelines for notebooks, Python packages, and ML models using GitHub Actions or similar tools.
• Partner with analysts to migrate Excel and SQL workflows to Databricks notebooks and jobs.
• Work with AWS partners to implement infrastructure as code using CDK or Terraform.
• Develop and maintain internal libraries, notebook templates, and utility functions for reproducible analysis and modeling.
• Establish best practices for version control, testing, logging, and monitoring of data workflows.
• Create and deliver documentation, playbooks, and internal training to improve team-wide adoption and fluency.

Skills & Experience

• Deep familiarity with Databricks (administration and development)
• Strong experience with CI/CD tools and pipelines for data science
• Solid understanding of AWS services (e.g. EC2, S3, Lambda, Glue) and CDK
• Proficient in Python and PySpark; SQL fluency
• Experience with MLflow or other model lifecycle tools
• Effective communicator and trainer — able to help others upskill
• Comfortable building internal tools and documentation

Nice to Have

• Experience with Terraform, dbt, or Great Expectations
• Exposure to software engineering best practices in a collaborative environment
• Knowledge of data governance and compliance practices

Benefits

• Generous time off: 25 days holiday plus bank holidays and a company-wide end-of-year break.
• Flexible work environment: Hybrid working model with flexible hours.
• Comprehensive perks: Annual bonus scheme, pension, regular social events, birthday treats, and a volunteering policy.
• Growth opportunities: Lots of learning and development opportunities