Principal Data Scientist - Deep Learning

Jampp·Spain - RemoteRemotesenior

In short

→Liderazgo técnico en diseño de arquitecturas de deep learning para anuncios en tiempo real.
→Desarrollo de embeddings para entidades de alta cardinalidad con impacto directo en millones de clics por segundo.
→Transformar modelos manuales en arquitecturas basadas en representaciones aprendidas, en un sistema de escalabilidad mundial.

Fluent English (written and spoken) required for collaboration across global teams.

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What they ask for

✓Experiencia sólida en deep learning y modelado con DNNs.
✓Conocimiento avanzado en embeddings y representaciones aprendidas.
✓Experiencia práctica con modelos en producción bajo alto rendimiento y baja latencia.
✓Capacidad para definir estrategias técnicas y guiar equipos multidisciplinarios.
✓Experiencia en sistemas de publicidad programática y RTB.
✓Habilidades en evaluación y benchmarking de modelos a gran escala.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

Deep LearningDNN ArchitecturesEmbeddingsRepresentation LearningTime Series ForecastingGraph MiningCausal InferenceReal-Time Bidding (RTB)Programmatic AdvertisingMachine Learning at Scale

Who should you write to at Jampp?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

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 global 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 research silo. You will have the opportunity to work with massive, high-cardinality datasets, real-time bidding systems and models that directly influence how Jampp evaluates and bids on millions of advertising opportunities every second. WHAT YOU’LL DO • Define and drive the technical strategy for Jampp’s deep learning and embedding-based modeling architecture. • Design, develop and iterate advanced DNN architectures for prediction and optimization, initially focusing on CPI/CPA use cases and progressively expanding into real-time bidding, bid optimization, ranking and campaign optimization. • Define the architecture and strategy for learning rich representations of high-cardinality entities such as users, devices, creatives, publishers, advertisers, apps, campaigns and placements. • Lead the design of reusable embedding and representation-learning approaches that can support multiple models and use cases across the platform. • Identify opportunities to improve the performance, scalability and generalization of our machine learning models using raw signals and learned representations. • Develop modeling approaches for real-time prediction and decision-making in programmatic advertising, where models operate within strict latency and scale constraints. • Evaluate new modeling approaches and technologies, balancing state-of-the-art deep learning techniques with the practical requirements of large-scale DSP and RTB systems. • Establish technical standards and best practices for model development, experimentation, evaluation and productionization across the Data Science team. • Guide complex modeling initiatives from problem definition and experimentation through production deployment and continuous improvemen

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