Ad Monetisation Manager

About the role

This position owns the end-to-end ad monetization strategy and revenue generation for a diverse mobile game portfolio, with a primary mandate to drive nine-figure annual performance. The role leverages artificial intelligence as a core accelerator to achieve market-leading speed in experimentation and decision-making, while granting the manager full ownership and autonomy over the monetization roadmap. You will act as the central authority on yield management, responsible for translating raw performance data into high-impact strategic actions that directly influence the bottom line. Success in this role is defined by your ability to navigate complex ad ecosystems and maximize value across multiple titles simultaneously. The position requires a proactive mindset that anticipates market shifts and adjusts tactics accordingly to maintain a competitive edge. You will own the critical relationship between product quality and revenue integrity, ensuring that optimization never comes at the cost of player experience. Ultimately, this role is pivotal in steering the financial trajectory of the company's gaming operations through sophisticated data-driven leadership.

Key facts

What you'll do

Ownership of ad monetization strategy and revenue is exercised across mobile game portfolios to achieve nine-figure annual performance.

Bidding and waterfall configurations within MAX Mediation are managed, experimented with, and shipped rapidly to optimize yield.

Revenue outcomes are driven through proactive negotiation with ad partners and resolution of technical issues such as SDK updates, discrepancies, and creative quality.

Ad performance reporting for eCPM, ARPDAU, and IMPDAU is built and automated using AI and scripts to accelerate insights.

Cross-functional collaboration with product and engineering optimizes ad product monetization outcomes.

Technical oversight of ad server setups and key integrations is conducted to ensure data accuracy and operational stability.

Forecasting and pacing plans are constructed and iterated upon using AI tools to align revenue targets with market conditions.

Identification of growth opportunities is performed through deep analysis of segment performance and user lifetime value signals.

Process efficiency is increased by automating manual reporting workflows and eliminating redundant data handling tasks.

Stakeholder communication is facilitated to align on priorities, interpret results, and drive swift action on recommendations.

Requirements

1+ year in mobile ad monetization is required, or less if learning speed is unusually high.

A solid understanding of mobile gaming economics and waterfall optimization is necessary.

Obsessive data investigation paired with automation to avoid repetitive manual work is expected.

Daily use of AI tools in work is required instead of following directives.

Thriving with goals and trust occurs without the need for daily task lists.

Fluency in written and spoken English is mandatory.

Practical notes

This role is not for those who need detailed instructions for every task. Decision making must continue comfortably under incomplete information. AI is treated as a daily tool rather than a buzzword. A relocation package with visa support is provided for eligible candidates. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role centers on rewarded mobile gaming and operates within a fast-moving gaming ecosystem. The company is headquartered in Berlin and backed by a major gaming enterprise while maintaining independent operations. Tools commonly used include AI scripts and mediation platforms. The environment emphasizes outcomes, speed, and cross-cultural collaboration.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.