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 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.

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.

Act as a strategic analytical partner to Product, Data Science, and Business leads. You proactively translate ambiguous business challenges into analytical roadmaps and turn complex ML insights into clear recommendations that shape product strategy.

Challenge product and technical decisions with data, bringing an independent analytical perspective to model development, experimentation and product strategy.

Drive analytical projects with end-to-end ownership: from initial scoping and hypothesis generation to stakeholder alignment and post-decision impact. Establish reusable frameworks and best practices that raise the bar for how we use data across teams.

Mentor and guide other analysts on the team, fostering a culture of high analytical standards, strong business intuition, and effective communication.

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, or Data Science, with a proven track record of delivering measurable business impact and working closely with senior cross-functional stakeholders.. Experience in AdTech, mobile apps or ML-driven products is a strong plus.

You’re proficient in SQL and comfortable working with large datasets.

You have strong Python 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.