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.
Key Responsibilities include:
Advanced Analytics & Machine Learning
- 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.
Generative AI & LLM Applications
- 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.
Product Ionization & AI Engineering
- 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.
Automation & AI Framework Development
- 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.
Qualifications & Experiences:
- 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+experience in GenAI
- Demonstrated track record of delivering production-grade AI/ ML solutions with measurable business impact.
Generative AI & Large Language Model (LLM) Expertise
- 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.
- Exposure to multimodal AI (text, image, audio, video) and agent-based workflows is a plus.
- 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.
