> Markdown version of [/jobs/ext/3359269-sr-principal-data-scientist-agentic-ai](https://www.wearedevelopers.com/jobs/ext/3359269-sr-principal-data-scientist-agentic-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Principal Data Scientist (Agentic AI) - **Company:** Warner Bros. Discovery - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Cloud Engineering, Content Analysis, Continuous Integration, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Software Architecture, Recommender Systems, Tensorflow, SQL Databases, Data Streaming, User-Centered Design, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Snowflake, Multi-Agent Systems, Build Management, Information Technology, Machine Learning Operations, Virtual Agents - **Published:** September 1, 2026 - **Apply:** https://www.themuse.com/jobs/warnerbrosdiscovery/sr-principal-data-scientist-agentic-ai ## About the Role This role is designed for an elite practitioner with 15-18+ years of experience, including 10-14 years of deep, hands-on expertise in Data Science, ML, and AI systems at enterprise scale. Unlike leadership or management roles, this position is purely an IC role, focused on technical depth, architectural rigor, and scientific excellence, without formal people-management responsibilities., * Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, Operations Research, or related disciplines. * 18-20 years of total experience, with 13-15 years in Data Science/ML, including hands-on technical leadership. * Deep expertise in: + Predictive modeling, optimization, and advanced ML techniques + MLOps and large-scale model deployment + Modern cloud ecosystems (AWS/GCP/Snowflake) + Python, PyTorch, TensorFlow, SQL, ML frameworks + Experiment design, causal inference, and statistical modeling * Demonstrated experience in Media & Entertainment, streaming, digital advertising, or consumer intelligence. * Strong track record of delivering enterprise-impact through AI solutions. * Exceptional communication skills, including the ability to influence executives and inspire technical teams. Preferred * Experience developing or customizing Large Language Models or multimodal foundation models. * Patent, publication, or conference-track record in ML/AI. * Experience with video intelligence, CV for media workflows, or content metadata systems. * Experience partnering with product and engineering organizations in a fast-paced environment. ## Description Design and build enterprise-grade Agentic AI systems, including autonomous and multi-agent workflows that leverage reasoning, tool use, planning, memory, and orchestration to execute complex business processes. Establish scalable patterns for agent architecture, evaluation, observability, guardrails, and human-in-the-loop controls to enable reliable and responsible deployment of AI agents in production., * Act as one of WBD's most senior technical ICs in Data Science and Machine Learning. * Lead the end-to-end design and implementation of advanced ML systems across: + Content intelligence & metadata enrichment + Audience modelling & personalization + Forecasting, optimization, and experimentation + Advertising intelligence & monetization analytics * Set technical direction and standards for complex ML implementations without direct people management. 2. Advanced Modelling & Scientific Excellence * Design and implement state-of-the-art models, including: + Large-scale recommender systems + Time-series forecasting & probabilistic models + Causal inference, experimentation & uplift modelling + NLP, generative AI & multimodal ML systems + Computer vision & video intelligence pipelines * Apply rigorous statistical thinking, experimentation discipline, and scientific validation to all solutions. * Serve as a final technical reviewer for high-risk or high-impact ML solutions. 3. Architecture of Scalable ML Systems * Architect production-grade ML systems integrated with WBD's cloud data ecosystem (AWS, Snowflake, GCP). * Define best practices for: + Feature engineering & feature stores + Model lifecycle management & MLOps + CI/CD for ML, model monitoring, and drift detection + Reproducibility, governance, and responsible AI * Partner deeply with data engineering, platform, and product engineering teams to ensure scalable, resilient delivery. 4. High-Impact Business Problem Solving * Own and deliver mission-critical AI solutions across: + Content performance prediction & ratings intelligence + Marketing attribution & lifecycle analytics + Search, discovery & ranking systems + Ad load optimization & pricing intelligence + Operational forecasting & automation * Translate complex modelling outputs into clear, executive-ready insights that drive decisions. 5. Executive & Cross-Functional Influence (Without Line Management) * Serve as a trusted technical advisor to senior leaders across Streaming, Content, Ad Sales, Marketing, and Technology. * Communicate complex ML concepts with clarity and credibility to non-technical stakeholders. * Influence enterprise AI roadmaps, architectural decisions, and investment priorities through expertise-not hierarchy. 6. Technical Mentorship & Community Leadership * Mentor senior and staff-level data scientists through technical guidance, design reviews, and deep problem-solving. * Contribute to internal AI communities of practice, technical forums, and standards bodies. * Elevate overall engineering and scientific rigor across the Data Science organization. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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