Machine Learning Intern: Portfolio Optimization & Simulation Operations

Our common vision

Empowering marketers to make data-driven decisions with a copilot that is transparent, independent and powered by artificial intelligence.

About the role

Nexoya runs AI-driven budget optimizations and budget simulations for marketing portfolios of enterprise customers across many ad channels. Every optimization and simulation that reaches a customer has to be correct, explainable and on time. As our ML Intern, you own the day-to-day operation of these runs: you execute them, check their quality, investigate when something looks wrong, and turn recurring manual work into reliable tooling. You work closely with our ML engineers and with Customer Success, and you see directly how your work changes the budget decisions of our customers.

Your responsibilities

Run and monitor portfolio optimizations

• Execute and monitor scheduled and on-demand optimization runs across customer portfolios.
• Check optimization proposals for plausibility before they reach customers: budget shifts, expected KPI changes, channel constraints and funnel-level effects.
• Investigate failed or unexpected runs, identify the root cause (data, configuration or model) and resolve it or escalate it with a clear diagnosis.

Prepare and analyze budget simulations

• Set up and run budget scenario simulations on request from Customer Success, for example for budget planning, seasonal shifts or new channel tests.
• Summarize simulation results in a form that account managers can explain to customers.
• Compare simulated outcomes with realized performance and document deviations.

Safeguard data and model quality

• Monitor input data for gaps, anomalies and tracking changes that affect optimization quality.
• Track model fit and prediction accuracy per portfolio and flag degradation early.
• Maintain portfolio configurations such as funnel steps, constraints and target definitions together with the responsible teams.

Automate the operational workflow

• Replace manual checks with Python scripts, validation rules and alerts.
• Build and maintain dashboards that show run status, data health and model performance across portfolios.
• Write runbooks so that operational knowledge is shared, not held by one person.

The impact you will have

• Customers receive optimization proposals and simulations that are correct and delivered on time.
• Issues are caught before customers see them, not after.
• The time the ML team spends on operational work goes down measurably, because recurring steps are automated.
• Customer Success gets faster and clearer answers to simulation requests.

Your experience and skills

• Currently enrolled in or recently graduated from a Bachelor's or Master's program in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related field.
• Solid Python skills, including pandas and NumPy, and comfort working with SQL.
• Good understanding of statistics and basic machine learning concepts such as regression, model evaluation and overfitting.
• A careful, structured way of working: you check results before you share them and you document what you do.
• Ability to explain technical findings clearly to non-technical colleagues.
• Fluent English. German or Italian is a plus.

Bonus points

• Experience with optimization methods, for example constrained or convex optimization.
• Familiarity with Marketing Mix Modeling, attribution or digital advertising metrics.
• Experience with data pipelines, scheduled jobs or monitoring tools such as Datadog.
• Experience with Git, Docker or cloud environments.

Your future work life

• Purpose: Your work directly influences how enterprise marketers allocate their budgets.
• Responsibility: You own a defined operational area from day one, with a personal development roadmap and regular feedback.
• Learning: You work alongside experienced ML and data engineers on production systems, not on side projects.
• Culture: A transparent company culture at eye level, where every person is valued and supported.
• Strategy: You take part in shaping company strategy and culture during our quarterly Strategy Days.
• Events: Regular team events, on-site and off-site.

Application process

1. Profile review and short phone screen
2. Take-home case study on a portfolio optimization scenario
3. Case discussion with the ML team
4. On-site interview and team evaluation

Offer