Senior Machine Learning Engineer

About the role

NielsenIQ, a consumer intelligence company that uses machine learning and artificial intelligence to understand consumer behaviour and market trends, is looking for a Senior Machine Learning Engineer to join its Tech & Durable Global Data Science team. The role turns innovative ML research into scalable, production-ready systems that power global decision-making, working at the intersection of data science and engineering.

Key responsibilities

• Design, develop, test, deploy and maintain machine learning solutions using software engineering best practices.
• Turn data science prototypes into scalable, production-ready systems.
• Implement and manage end-to-end ML workflows with MLOps practices in cloud and on-premise environments.
• Partner with data scientists, software engineers and product experts in international teams.
• Develop tools, methods and best practices to raise ML engineering standards.
• Mentor colleagues, contribute to internal Communities of Practice and take part in training and cross-functional learning.
• Influence the direction of ML engineering through strategic initiatives and technical roadmaps.

Required profile

• Degree in computer science, engineering, statistics or a related field (BSc, MSc or PhD).
• 4+ years of experience in machine learning software development.
• Solid experience in large-scale database environments and with production-level code quality.
• Understanding of statistical methods and machine learning algorithms.
• Stakeholder management and communication skills, and ability to work independently in a distributed team.
• Professional working proficiency in English.

Required skills

• Python and ML libraries and frameworks
• Containerization and orchestration (Docker, Kubernetes)
• Argo, Kubeflow, MLflow, GitLab CI, Prometheus, Grafana
• PostgreSQL, BigQuery and RDBMS
• Nice to have: cloud environments (AWS, GCP), MLflow, agile practices, forecasting, pricing, revenue assurance or media analytics background

What we offer

• Hybrid work and flexible working environment
• Ongoing training and opportunities for personal and professional growth
• Volunteer time off and LinkedIn Learning
• Employee Assistance Program