Machine Learning Engineer

You will join international and collaborative team, with colleagues based across the US, Germany, Poland, and Italy. The team delivers innovative solutions in the field of market research, with a strong focus on brand management.
Our team combines advanced statistical modeling with hands-on software development. We start by designing and validating Proof of Concepts (PoCs) that address real business challenges, and then transform these prototypes into robust, scalable products used globally. This means your work will not end at experimentation—you will be directly involved in delivering and maintaining production-grade solutions that create impact at scale.
As a Machine Learning Engineer, your role bridges data science and software engineering. You will take ownership of turning PoC models into reliable, maintainable, and scalable systems. This includes designing production architectures, optimizing performance, ensuring code quality, and enabling seamless deployment across global environments.
This is a role for someone who enjoys both experimentation and engineering—someone who wants to see their models evolve from initial ideas into fully operational products used worldwide.
Responsibilities
· Design, develop, test, deploy, maintain and enhance data science solutions using software engineering best practices
· Study and convert data science prototypes to production quality DS/ML Solution
· Implement machine learning solutions at a large scale, in on-premises or cloud-based environments
· Create and improve methods and tools and contribute to best practices in the area of Data Science
· Foster ML related knowledge exchange within NIQ, especially around code quality and engineering best practices related knowledge exchange within NIQ
· Present NIQ’s MLE / DS expertise at conferences and workshops
· Master or Bachelor degree that reflects computer science / engineering and statistical / mathematical skills
· Typically 2+ years of work experience
· Solid experience with production-level code quality metrics and working closely with software and testing engineers
· Expert skills in Python and the respective statistical ecosystem
· Solid statistical skills:
either in survey statistics (sampling, non-response adjustments, Bayesian statistics),
or econometric modelling (program evaluation, forecasting, economic theory, panel data),
or observational causal inference (potential outcomes, structural equation models, instrumental variables)
or experimental design
· Experience working with docker

Employment type
Full-time

Work arrangement
Hybrid