ML Intern: Optimization Quality & Monitoring
Overview
Nexoya offers an internship opportunity for an ML Intern focusing on optimization quality and monitoring. The role is based in Zürich, Switzerland. The organization provides AI-powered attribution and budget optimization to help marketing teams across various industries grow their bottom line.
Who can apply
Candidates must have recently completed a Master degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field. Applicants are required to have solid Python skills, including pandas and NumPy, along with comfort working with SQL. A good understanding of statistics and basic machine learning concepts such as regression, model evaluation, and overfitting is necessary. Candidates must possess a careful and structured way of working, the ability to explain technical findings clearly to non-technical colleagues, and fluent written and spoken English. Visa sponsorship may be offered for suitable candidates.
What you get
Interns own the quality of a defined part of the automation from day one, accompanied by a personal development roadmap and regular feedback. Participants work alongside experienced machine learning and data engineers on production systems. The experience includes taking part in company strategy and culture during quarterly Strategy Days, as well as regular on-site and off-site team events.
Requirements
The role involves monitoring automated prediction, optimization, and simulation pipelines across customer portfolios, tracking run health, completeness, and timeliness. Responsibilities include verifying that predictions and optimization proposals meet quality standards, investigating failed or unexpected runs to identify root causes, and defining plausibility checks for budget planning, seasonal shifts, or new channel tests. Interns compare optimized and simulated outcomes with realized performance, monitor input data for gaps and anomalies, and track model fit and prediction accuracy. Additional duties include maintaining portfolio configurations, automating operational workflows using Python scripts, validation rules, and alerts, building dashboards, and writing runbooks. Bonus qualifications include experience with optimization methods, Marketing Mix Modeling, attribution or digital advertising metrics, data pipelines, scheduled jobs, monitoring tools, Git, Docker, cloud environments, and fluency in German or Italian.
How to apply
The application process consists of a profile review and a short phone screen, followed by a take-home case study. Selected candidates proceed to a case discussion with the machine learning team, concluding with an on-site interview and team evaluation.
Key dates
The source does not provide specific dates or deadlines for this opportunity.