Data Analyst, ML & Experimentation

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.

• 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.

Fuel for the Journey: Benefits to Support Your Ambitions

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.