GEN AI - DATA SCIENTIST
📍
McKinney, TX
🧪
Not Applicable
About The Role
The Coordinated Structural Pricing and Simulator team is at the center of managing Uber's pricing and marketplace balance all over the world. Our team is responsible for controlling the balance of Mobility pricing in order to aggressively achieve growth, while maintaining a healthy marketplace. We achieve this goal by employing a variety of techniques, including machine learning, experimentation and causal-ML, economic modeling, and simulation and optimization. Our problem space sits at the intersection of machine learning and economics, and is a unique opportunity to leverage rich data to build high-quality simulations and do sophisticated policy optimizations. We are actively working on a new pricing and simulation tech focused on highly accurate forecasting and exploring many different avenues for simulation, both ML-driven and structural economics-driven. We are looking for candidates who are proficient in coding and building numerical models and simulation, as well as those who have prior experience in one or more of the areas: experimentation and causal ML, economic modeling, supervised machine learning, and reinforcement learning.
What The Candidate Will Do
The Coordinated Structural Pricing and Simulator team is at the center of managing Uber's pricing and marketplace balance all over the world. Our team is responsible for controlling the balance of Mobility pricing in order to aggressively achieve growth, while maintaining a healthy marketplace. We achieve this goal by employing a variety of techniques, including machine learning, experimentation and causal-ML, economic modeling, and simulation and optimization. Our problem space sits at the intersection of machine learning and economics, and is a unique opportunity to leverage rich data to build high-quality simulations and do sophisticated policy optimizations. We are actively working on a new pricing and simulation tech focused on highly accurate forecasting and exploring many different avenues for simulation, both ML-driven and structural economics-driven. We are looking for candidates who are proficient in coding and building numerical models and simulation, as well as those who have prior experience in one or more of the areas: experimentation and causal ML, economic modeling, supervised machine learning, and reinforcement learning.
What The Candidate Will Do
- Design and build Machine Learning models with optimization engines.
- Productionize and deploy these models for real-world application.
- Review code and designs of teammates, providing constructive feedback.
- Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product.
- Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 2+ years of full-time engineering experience or PhD new grad
- Experience working with multiple multi-functional teams(product, science, product ops etc).
- Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++).
- 1+ year of ML/economics experience and building ML/economic models
- Experience with the design and architecture of ML systems and workflows.
- Experience with building algorithmic solutions in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments.
- Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact.
- Experience with optimizing Spark queries for better CPU and memory efficiency.
- Working knowledge of latest ML technologies, and libraries, such as PyTorch, TensorFlow, JAX, Ray, etc.
- Experience owning and delivering a technically challenging, multi-quarter project end to end.
- Experience with big-data architecture, ETL frameworks and platforms, such as HDFS, Hive, MapReduce, Spark, , etc.