Full description
adjoe builds the technologies behind mobile apps growth and monetization. With our core product Playtime Arcade, we've become the global leader in rewarded advertising, an ad unit built on a simple premise: users earn real in-app rewards for engaging with new apps. The result is one of the most effective value exchanges in adtech, connecting advertisers and publishers with over 770 million users annually.
Architecting Intelligence to Optimize 200M+ Daily Decisions
As the intelligence core of our engineering organization, our Data Science team doesn't just deploy models, we engineer the fundamental decision engine that powers our platform. At a scale of 770 million users and 100,000+ predictions per second, we are solving a multi-objective optimization problem that balances user incentives, advertiser ROI, and long-term platform health in real time.
Our architecture is built on a 1PB+ behavioral data lake, providing the high-fidelity input necessary to train deep learning models that predict individual user engagement with precision. We aren't just optimizing clicks, we are dynamically calculating optimal reward structures to sustain a global value exchange.
Engineered for performance, our stack leverages Tensorflow and PyTorch for model training, NVIDIA Triton to achieve sub-100ms inference. We own the full ML lifecycle from high-level research and feature engineering to deployment and A/B experimentation. Here, you will find the autonomy, the data depth, and the massive scale required to solve the most complex optimization challenges in the adtech ecosystem.
The data infrastructure behind this runs at real scale: 2TB ingested in real time every day, 100+ Airflow jobs and data pipelines, and ML models handling p99 latency of 100ms across 100,000+ predictions per second.
What you will do:
Analyze and evaluate the performance of our machine learning models using user-level and aggregated data, identify inefficiencies and failure patterns, and turn findings into actionable recommendations for our Data Science teams.
Investigate how our ML models perform in the real world and compare model assumptions and predictions with actual user and business behavior. Identify gaps between offline model performance and online outcomes and help the team close them.
Define and improve metrics for measuring ML model performance and business impact, working closely with Data Scientists and Product Managers to ensure we optimize for the right outcomes.
Design, prepare and analyze A/B tests and other experiments to measure the impact of new models, features and algorithmic changes. Assess statistical significance, experiment quality and practical business impact.
Build dashboards and automated reports that make ML model performance, experiments and key business metrics transparent and easy to understand for technical and non-technical stakeholders.
Act as a bridge between Data Science, Product and Business teams, translating business questions into analytical problems and turning complex ML insights into clear recommendations and decisions.
Challenge product and technical decisions with data, bringing an independent analytical perspective to model development, experimentation and product strategy.
Document your analyses, methodologies and findings and build reusable analytical approaches that can be applied across models and experiments.
Who you are:
You have a degree in mathematics, statistics, physics, computer science, economics, analytics, or a similar quantitative field.
You have 5+ years of relevant experience in Data Analytics, Product Analytics, Experimentation, Data Science or a similar role. Experience in AdTech, mobile apps or ML-driven products is a strong plus.
You have a strong analytical mindset and enjoy digging into data to understand why something happens, not just what happened.
You’re proficient in SQL and comfortable working with large datasets.
You have strong Python or R skills and are comfortable using them for data analysis and statistical modelling.
You have a solid understanding of statistics and experimentation, including hypothesis testing, confidence intervals, statistical significance and A/B test analysis.
You have an understanding of machine learning concepts and model evaluation and are comfortable working with Data Scientists. You don't necessarily need to build ML models yourself, but you should understand how to assess their performance and limitations.
You have experience working with model and business metrics, and can connect technical model performance to real-world business outcomes.
You have excellent data visualization skills and experience building dashboards using tools such as QuickSight, Power BI, Tableau, R Shiny, Streamlit or similar.
You are comfortable communicating complex analytical findings to both technical and non-technical audiences.
Fuel for the Journey: Benefits to Support Your Ambitions
Invest in Your Future: Regular feedback and our development program support your growth, helping you expand your skill set and achieve your career goals.
Easy Arrival to adjoe: From signing to settling in Hamburg, we’ve got you covered. Need a visa? No problem. Ready to build your new life and career at adjoe in Hamburg? We support every ambition—from learning German to a relocation bonus that helps you settle in and make Hamburg feel like home.
Live Your Best Life, at Work and Beyond: We work in a hybrid setup with 3 core office days, plus flexible working hours. Enjoy 30 vacation days, 3 weeks of remote work per year, and free access to an in-house gym with lots of different fitness classes and mental health support through our Employee Assistance Program (EAP).
Thrive Where You Work: Enjoy the Alster lake view from our central office with top notch equipment, fun open spaces, and a large variety of snacks and drinks.
Join the Community! Participate in regular team and company events, including hackathons and social gatherings. We work together, and we celebrate together, too.
Skip writing cover letters. Tell us about your most passionate personal project, your desired salary and your earliest possible start date. We are looking forward to your application!
We welcome applications from people who will contribute to the diversity of our company.
We welcome applications from people who will contribute to the diversity of our company.
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