Senior Data Scientist - Applied AI Job at Uber (Sao Paulo)

Job Description

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.

Job Responsibility

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

Requirements

5+ years of experience (or Ph.D. equivalent) in an Applied Science, Machine Learning, or Data Science role
Specialized domain expertise in Ranking, Recommender Systems (RecSys), or Search
Proven experience in training and deploying Deep Learning models at scale within a production environment
Proficiency in Python and SQL with experience handling large-scale datasets using Spark, Hive, or PySpark
Solid understanding of statistical methods, experimental design, and A/B testing
BSc., M.S., or Ph.D. in Computer Science, Machine Learning, Statistics, Economics, or a related quantitative field

Nice to have

Experience with advanced modeling techniques like Reinforcement Learning, multi-task learning, or auto-regressive models
Ability to communicate complex scientific results to both technical and non-technical stakeholders to influence business strategy
Familiarity with deploying production-grade pipelines into real-time, low-latency systems using Kafka or Pinot
Strong systems thinking and the ability to make smart trade-offs between short-term velocity and long-term scientific rigor

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Senior Data Scientist - Applied AI

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