Staff, Data Science & Applied AI

Warner Bros Discovery Warner Bros Discovery · Media · Atlanta, GA +1 · Technology

Staff Data Scientist & Applied AI role focused on designing, developing, and deploying advanced analytics and AI capabilities, with a strong emphasis on Generative AI and LLM applications. Responsibilities include building RAG pipelines, fine-tuning models, implementing evaluation frameworks, and productionizing solutions in cloud environments. The role requires translating business challenges into scalable analytical solutions and AI products.

What you'd actually do

  1. Design, develop, and deploy statistical, predictive, and machine learning models across domains such as customer analytics, forecasting, personalization, optimization, and content performance.
  2. Design and implement Generative AI solutions leveraging large language models (LLMs) for use cases such as knowledge retrieval, content intelligence, metadata enrichment, summarization, and workflow automation.
  3. Architect Retrieval-Augmented Generation (RAG) pipelines integrating structured and unstructured enterprise data sources.
  4. Fine-tune or adapt foundation models where appropriate using parameter-efficient techniques (e.g., LoRA, adapters) aligned with business needs.
  5. Implement evaluation pipelines to measure hallucination rates, bias, latency, cost efficiency, and model quality in production environments.

Skills

Required

  • 8+ years relevant experience in data science
  • 2+ years experience in GenAI
  • Designing and deploying Generative AI solutions using large language models
  • Prompt engineering
  • Structured output design
  • Few-shot learning strategies
  • Systematic prompt optimization
  • Building Retrieval-Augmented Generation (RAG) pipelines
  • Integrating vector databases and enterprise data sources
  • Fine-tuning or adapting foundation models using parameter-efficient approaches
  • LLM evaluation methodologies
  • Responsible AI principles
  • data privacy considerations
  • model governance requirements

Nice to have

  • multimodal AI (text, image, audio, video)
  • agent-based workflows
  • enterprise AI platforms (e.g., AWS Bedrock, Azure OpenAI, Databricks Model Serving, Snowflake Cortex, or equivalent)

What the JD emphasized

  • production-grade AI/ ML solutions
  • Generative AI solutions
  • large language models (LLMs)
  • Retrieval-Augmented Generation (RAG) pipelines
  • evaluation pipelines
  • fine-tune or adapt foundation models
  • parameter-efficient techniques
  • Responsible AI principles
  • data privacy considerations
  • model governance requirements in regulated environments

Other signals

  • Deploying production-grade AI/ML solutions
  • Designing and implementing Generative AI solutions
  • Architecting RAG pipelines
  • Fine-tuning foundation models
  • Implementing LLM evaluation pipelines