Descripción de la oferta
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 300 TB of data per day, across three global data centers. We rely on real-time machine learning models 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 Senior Data Scientist, you will play a key role in this transformation. You will work hands-on on the design, development and deployment of DNN-based models that power prediction, bidding and optimization. You will collaborate closely with Data Scientists, ML Engineers, Software Engineers and Data Engineers to turn research ideas into production-ready solutions with measurable impact on our core product. This is not a research silo. You will have the opportunity to work with massive, high-cardinality datasets, real-time systems and models that directly influence how Jampp bids on millions of advertising opportunities every second. WHAT YOU’LL DO Design, develop and iterate deep learning models for prediction and optimization. Develop embedding-based approaches to represent high-cardinality entities such as users, devices, creatives, publishers, advertisers, apps, campaigns and placements. Identify opportunities to improve the performance, scalability and generalization of our machine learning models using raw signals and learned representations. Experiment with different modeling approaches, evaluate results rigorously and translate successful experiments into production solutions. Design, code and deploy machine learning models and supporting production tools, primarily in Python. Work with large-scale datasets and contribute to the data pipelines, feature infrastructure and feedback loops required to train and continuously improve our models. Collaborate closely with ML Engineers, Data Engineers and Software Engineers to ensure models can be trained, served and monitored reliably in production. Analyze model and product performance metrics to understand how changes in our algorithms impact bidding, campaign performance and business outcomes. Communicate technical findings and trade-offs clearly, and collaborate with other Data Science teams to share knowledge and best practices. REQUIREMENTS +5 years of experience in Data Science, Machine Learning or a closely related quantitative role. Strong academic background in Applied Mathematics, Physics, Statistics, Computer Science, Econometrics, or another quantitative field. Strong experience with Python and the scientific/machine learning Python ecosystem. Hands-on experience developing and evaluating machine learning models, with a solid understanding of statistical and machine learning fundamentals. Practical experience with Deep Learning and neural network architectures. Experience working with large-scale datasets and/or high-cardinality categorical or ID-based features. Ability to take models from experimentation through production, working closely with engineering teams. Strong analytical and problem-solving skills, with the ability to independently investigate ambiguous problems and turn them into actionable solutions. Comfortable conducting daily professional communications in English (written and verbal). You have experience building and deploying deep learning models using embeddings or other representation-learning techniques. You have experience working on recommendation systems, ad-tech, bidding, pricing, ranking, personalization, fraud detection, or other optimization problems at scale. You have experience with highly imbalanced datasets and/or large-scale prediction problems. You have worked with models operating in real-time or other latency-sensitive environments. You have experience designing or contributing to ML infrastructure, feature stores, training pipelines, model serving or monitoring systems. You enjoy working on problems involving billions of data points, millions of entities and complex interactions that cannot easily be captured through manually designed features. You have pride in working on projects from experimentation through successful production deployment, involving a wide variety of technologies, systems and modeling techniques. You like working in a self-sufficient, autonomous manner, striving through ambiguity and taking ownership of your work. You have a sense of urgency and ownership over the product, and care about measuring the real-world impact of your solutions. You are curious, pragmatic and comfortable balancing technical depth with practical business impact. Smarts, humility, and equal willingness to learn and teach. #J-18808-Ljbffr
