We are looking for a Data Scientist to join our Los Angeles based headquarters, and work on exciting personalization initiatives. The ideal candidate has a practical background in machine learning and deep learning, with recent hands-on experience in natural language understanding, large language models (LLMs), generative AI applications, retrieval-augmented generation (RAG), or agentic AI systems.
Requirements
- Processing, cleansing, and verifying the integrity of data used for analysis
- Doing ad-hoc analysis and presenting results in a clear manner
- Experience with feature synthesis and selection techniques to build models
- Solid understanding of regression, classification algorithms framework
- Apply machine learning techniques at scale on massive data sets
- Working with large language models (LLMs) for applications such as content summarization, recommendation, and personalization
- Apply RAG (Retrieval-Augmented Generation) pipelines for knowledge retrieval
- Experiment agentic AI framework to enhance personalization and automation
- Prior work with model evaluation benchmarks and large-scale LLM testing frameworks
- Experience with prompt fine-tuning techniques, LLMs optimization techniques(pre-training, post-training etc)
- Collaborate with cross-functional agile teams of software engineers, domain experts, and others to build new product features for multiple business units
- Collaborate with data scientists across a variety of businesses to prioritize and promote our machine learning efforts
Benefits
- health insurance options such as medical, dental, and vision coverage
- flexible spending accounts (FSA) for medical and dependent care
- short-term and long-term disability insurance
- life and AD&D insurance
- 401(k) retirement savings plan with a company match
- paid time off (PTO)
- paid holidays
- commuter benefits
- access to our Employee Assistance Program (EAP) and well-being coaching services
- voluntary benefits such as home, auto and pet insurance, and discounted legal and financial services
