Staff, Data Science & Applied AI

Warner Bros. Discovery•Atlanta, GA

About The Position

As Staff, Data Science & Applied AI, you will be a core technical contributor within the Enterprise Data & AI Solutions team supporting Warner Bros. Discovery’s global portfolio — including Studios, Streaming, Linear Networks, Consumer Products, Games, and Direct-to-Consumer platforms. This role is designed for a hands-on expert in applied data science who thrives at the intersection of statistical rigor, machine learning engineering, and business impact. You will translate complex business challenges into scalable analytical solutions, production-grade models, and data products that drive measurable enterprise value. You will operate as a senior individual contributor, partnering closely with Product, Engineering, and Business stakeholders to design, develop, deploy, and scale advanced analytics and AI capabilities across the organization.

Requirements

Bachelor’s degree, MS, or greater in Computer/Data Science, Engineering, Mathematics, Statistics, or related quantitative discipline.
8+ years relevant experience in data science
2+ years experience in GenAI
Demonstrated track record of delivering production-grade AI/ ML solutions with measurable business impact.
Hands-on experience designing and deploying Generative AI solutions using large language models (e.g., GPT-class models, open-source foundation models, or enterprise LLM platforms).
Strong proficiency in prompt engineering, structured output design, few-shot learning strategies, and systematic prompt optimization
Experience building Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise data sources.
Familiarity with embedding models, semantic search, and vector stores (e.g., Pinecone, Weaviate, OpenSearch, FAISS, or equivalent).
Experience fine-tuning or adapting foundation models using parameter-efficient approaches (e.g., LoRA, adapters, instruction tuning).
Understanding of LLM evaluation methodologies, including hallucination detection, bias assessment, response quality scoring, and cost-performance trade-offs.
Experience working with enterprise AI platforms (e.g., AWS Bedrock, Azure OpenAI, Databricks Model Serving, Snowflake Cortex, or equivalent).
Understanding of Responsible AI principles, data privacy considerations, and model governance requirements in regulated environments.

Nice To Haves

Exposure to multimodal AI (text, image, audio, video) and agent-based workflows is a plus.

Responsibilities

Design, develop, and deploy statistical, predictive, and machine learning models across domains such as customer analytics, forecasting, personalization, optimization, and content performance.
Apply advanced techniques including ensemble methods, gradient boosting, deep learning, NLP, time-series forecasting, and recommendation systems.
Ensure model robustness through rigorous validation, monitoring, and performance tracking.
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.
Develop and optimize prompt engineering strategies, evaluation frameworks, and guardrails to ensure high-quality, reliable outputs.
Architect Retrieval-Augmented Generation (RAG) pipelines integrating structured and unstructured enterprise data sources.
Fine-tune or adapt foundation models where appropriate using parameter-efficient techniques (e.g., LoRA, adapters) aligned with business needs.
Implement evaluation pipelines to measure hallucination rates, bias, latency, cost efficiency, and model quality in production environments.
Collaborate with Responsible AI and Governance teams to ensure compliance with enterprise AI policies, data privacy standards, and ethical AI practices.
Collaborate with Data Engineering and DevOps teams to productionize ML and GenAI solutions in scalable cloud environments.
Design CI/CD pipelines for model lifecycle management, including experimentation tracking, versioning, and automated retraining.
Implement monitoring frameworks for model drift, prompt drift, performance degradation, and data integrity.
Develop reusable ML and GenAI frameworks, accelerators, and internal utilities that improve productivity across teams.
Advance automation initiatives to reduce manual workflows and enhance analytical velocity.
Stay current with cutting-edge advancements in foundation models, multimodal AI, and agentic architectures to continuously elevate enterprise AI capabilities.