Senior Data Scientist - Applied AI

Senior Data Scientist - Applied AI Department: Data Science Team: Data Scientist Location: Sao Paulo, Brazil Type: Full-Time **About the role and team** Working at Uber means solving hard problems in a high-stakes, fast-moving environment. You’ll need to take ownership, stay adaptable, and build with both urgency and care. If you’re energized by challenge and motivated by real-world impact, this is where you’ll grow! As an Applied Scientist on the Discovery Science team, you will move the needle for the business through strong product execution at the intersection of ML research and marketplace algorithms. This isn't about tuning models in a vacuum; it’s about navigating the messiness of a multi-sided ecosystem where performance, safety, and scale are inseparable. You will partner with engineers to architect the next generation of RecSys, balancing technical rigor with the pressure of real-world traffic and shifting business priorities. **What you’ll do** - Design and implement ML models and objective functions that unify competing business interests like organic relevance and sponsored content into a single value space. - Act as the science lead for foundational machine learning initiatives, unblocking technical debt and optimizing feature engineering for high-scale, real-time systems. - Navigate the ambiguity of user behavior by designing sophisticated experiments and causal inference frameworks that go beyond standard A/B testing. - Collaborate across disciplines (Product, Engineering, and Data Science) to translate high-level business goals into theoretically sound and performant technical roadmaps. - Research and apply advancements in Deep Learning, Reinforcement Learning, and GenAI to solve complex, high-impact problems without a clear starting point. - Own your models end-to-end, from the first scientific hypothesis to debugging production issues in real-time, low-latency environments. **Time spent in the day** - 40% Algorithm development, model training, and deep learning research. - 30% Designing experimentation frameworks and performing causal inference analysis. - 20% Cro