Principal Data Scientist – Deep Learning

At Jampp, we’re on a mission to enable the mobile app economy to grow by building technology that supports ambitious companies—from gaming to commerce—to expand their app reach and accelerate mobile businesses.

Jampp tackles complex, large-scale technological challenges in mobile advertising. We process over 2,500,000 ad requests per second, handling over 300TB of data per day across three global data centers. Our real-time machine learning models, with billions of features, deliver predictions in under 100ms.

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 deep neural network (DNN) architectures for prediction and optimization, initially focusing on CPI/CPA use cases, then 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 to support multiple models and use cases across the platform.
• Identify opportunities to improve the performance, scalability, and generalization of machine learning models using raw signals and learned representations.
• Develop modeling approaches for real-time prediction and decision-making in programmatic advertising, adhering to 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 real-time bidding (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 improvement.
• Design, code, and deploy machine learning models and supporting production tools, primarily in Python, while staying hands-on with the most technically challenging aspects.
• Collaborate with ML Engineers, Data Engineers, and Software Engineers to shape the training infrastructure, data pipelines, feature infrastructure, serving architecture, and feedback loops for Jampp’s next-generation ML platform.
• Analyze model and product performance metrics to understand how algorithmic changes impact bidding decisions, campaign performance, user response, and business outcomes.
• Provide technical mentorship and guidance to Data Scientists, helping them navigate complex modeling problems and develop stronger approaches to experimentation and model design.
• Communicate technical findings, architectural decisions, trade-offs, and recommendations clearly to both technical and non-technical stakeholders.
• Collaborate with Data Science and ML teams across Jampp and Affle to identify opportunities for shared capabilities, knowledge, and machine learning solutions.

REQUIREMENTS

• Significant experience in Data Science, Machine Learning, Deep Learning, or a closely related quantitative/technical role, with a track record of leading complex machine learning initiatives.
• Strong academic background in Computer Science, Applied Mathematics, Physics, Statistics, Engineering, Econometrics, or another quantitative field.
• Deep understanding of machine learning and deep learning fundamentals, including neural network architectures, representation learning, optimization, and model evaluation.
• Extensive hands-on experience developing and deploying Deep Learning models in production environments.
• Strong experience with Python and the scientific/machine learning Python ecosystem.
• Experience working with large-scale datasets and high-cardinality categorical or ID-based features.
• Proven track record of taking machine learning models from experimentation and research through reliable production deployment.
• Experience designing or making significant technical contributions to ML architectures, training pipelines, model serving, or other machine learning infrastructure.
• Experience working with real-time or latency-sensitive machine learning systems, ideally in advertising, marketplaces, recommendations, or other high-throughput environments.
• Strong analytical and problem-solving skills, with the ability to independently investigate ambiguous problems and define effective technical solutions.
• Strong technical communication skills, with the ability to influence modeling and architectural decisions across multidisciplinary teams.
• Experience providing technical leadership, mentorship, or direction to other Data Scientists or engineers.
• Comfortable conducting daily professional communications in English (written and verbal).

YOU MAY BE A GREAT FIT IF...

• You have designed and deployed deep learning systems based on embeddings, representation learning, or other approaches for learning from high-cardinality entities.
• You have experience working on DSPs, programmatic advertising, real-time bidding (RTB), ad exchanges, ad networks, or other real-time advertising systems.
• You have experience applying machine learning to bidding, bid optimization, CTR/CVR prediction, ranking, campaign optimization, or other decision-making problems in advertising.
• You have experience with recommendation systems, ad-tech, pricing, ranking, personalization, fraud detection, or other large-scale optimization and prediction problems.
• You have worked with systems processing very large volumes of ad impressions, auction events, or real-time user and publisher signals.
• You have experience modeling highly cardinal entities and complex interactions across users, devices, apps, creatives, publishers, advertisers, campaigns, or similar entities.
• You have experience designing or evolving ML platforms, training pipelines, feature stores, model serving, or monitoring systems.
• You have worked with models operating at very high scale and under strict latency constraints.
• You have experience balancing model complexity and predictive performance with computational cost, scalability, and production constraints.
• You have a strong track record of turning research ideas into production systems and measuring their real-world impact.
• You enjoy defining technical approaches to problems where there is no obvious answer and where modeling decisions can have a significant impact on the product and the business.
• You are comfortable providing technical leadership while remaining hands-on with the most challenging modeling and engineering problems.
• You are able to influence technical decisions through strong reasoning, experimentation, and communication rather than relying solely on formal authority.
• You like working in a self-sufficient, autonomous manner, striving through ambiguity and taking ownership of complex technical problems.
• You have a strong sense of urgency and ownership over the product, and care deeply about the quality, scalability, and impact of the solutions you build.
• You are curious, pragmatic, and comfortable balancing technical depth with practical business impact.
• You possess smarts, humility, and an equal willingness to learn and teach.