WHO WE ARE At Jampp, we’re on the mission of playing a leading role enabling the mobile app economy to grow. How? We build technology to support the most ambitious companies - from gaming to commerce pioneers - to propel the reach of their apps and accelerate their mobile businesses. We solve the most complex and large-scale technological challenges in mobile advertising today. We process over 2,500,000 ad requests per second, which amounts to over 300TB of data per day, across three integral data centers. We rely on real-time machine learning models (with billions of features) that give us predictions in less than 100ms. WHY DO WE NEED YOU? Data, and how it is used, plays a central role at Jampp and is at the heart of how we make product, business, operational, and financial decisions. Our Data Science team tackles challenging problems across many technical disciplines, including time series forecasting, graph mining, algorithmic optimization on petabytes of data, causal inference with missing data, and machine learning at scale.



We are now taking our machine learning platform to the next level. We are evolving from models built around manually designed features towards a deep learning architecture based on embeddings, allowing our models to learn richer representations of users, devices, creatives, publishers, advertisers, apps, campaigns and placements. As a Principal Data Scientist – Deep Learning , you will play a key role in defining and leading this transformation. You will provide technical leadership across the development of our next-generation machine learning architecture, from the design of deep learning models and embedding strategies to their deployment and evolution in production.
You will work hands-on on some of the most challenging modeling problems in programmatic advertising, while setting technical direction, establishing best practices and helping other Data Scientists and engineers make sound architectural and modeling decisions. This is not a

📌 Principal Data Scientist (España)
🏢 Jampp
📍 España