- Design, develop, evaluate, and productionize machine learning models for personalization and recommendation systems.
- Advance client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation.
- Explore and apply LLMs, deep learning, representation learning, multimodal modeling, and generative approaches.
- Own the machine learning lifecycle from problem formulation and data exploration through modeling, experimentation, deployment, monitoring, and iteration.
- Design offline evaluations and online experiments to measure model performance and client and business impact.
- Work with large-scale behavioral and product datasets using Python, SQL, and distributed data-processing tools.
- Build production-quality ML solutions with attention to scalability, reliability, latency, observability, and cost.
- Collaborate with Product, Engineering, and Data Science teams to develop reusable foundational ML capabilities.
- Contribute to technical direction through design discussions, code reviews, research, prototyping, and best practices.
- Mentor and collaborate with Data Scientists and engineers, and communicate technical concepts and tradeoffs to stakeholders.
PythonSQLMachine Learning+4 more