Data scientists at Disney Direct-to-Consumer are the insights and modeling partners for the growth, content, marketing, product, and engineering teams across Disney+, Hulu and ESPN+. They leverage data, statistical methods, and machine learning to generate insights, predictions, and scalable solutions that inform critical decisions and shape the experiences of millions of viewers worldwide. Through model development, analysis, visualization, and data products, they build capabilities that are continuously refined through close collaboration with cross-functional business stakeholders.

As a Senior Data Scientist on the Content Understanding team, you will lead the design, development, and deployment of advanced NLP, multimodal machine learning, and large language model (LLM) solutions to support content classification, segmentation, metadata enrichment, similarity modeling, and related downstream applications across Disney+, Hulu, and ESPN+. This role requires deep expertise in modern deep learning architectures, hands-on experience adapting and evaluating foundation models, and a strong track record of delivering production-grade AI systems in partnership with engineering and cross-functional stakeholders.

Key Responsibilities

• Lead the design, development, evaluation, and deployment of advanced NLP, LLM, and multimodal ML solutions for content understanding use cases.

• Build and adapt models for tasks such as text classification, semantic similarity, retrieval, ranking, metadata enrichment, and multimodal understanding across text, image, and video.

• Fine-tune, customize, and optimize open-source and foundation models using modern techniques such as supervised fine-tuning, parameter-efficient tuning, retrieval-augmented generation, and embedding-based methods.

• Partner closely with product, engineering, analytics, and business stakeholders to translate ambiguous business needs into scalable machine learning solutions.

• Optimize training and inference workflows using GPU infrastructure, distributed systems, and production best practices.

• Drive production excellence through strong software engineering discipline, including testing, code review, CI/CD, orchestration, monitoring, and model lifecycle management.

• Contribute technical leadership through architecture decisions, best practices, experimentation strategy, and mentorship of other scientists.

Basic Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.

• 5+ years of industry experience building and deploying machine learning models in production environments.

• Strong expertise in deep learning, NLP, embeddings, transformer-based architectures, and large language model systems, including attention mechanisms, tokenization, representation learning, and modern evaluation methodologies.

• Proficiency in Python and at least one major deep learning framework such as PyTorch or TensorFlow.

• Experience with production ML systems, including CI/CD, job orchestration, containerization, monitoring, and MLOps practices.

• Experience evaluating ML systems using appropriate technical and product metrics, and balancing quality, latency, scalability, and maintainability.

• Strong communication skills, with the ability to explain technical concepts clearly to cross-functional and non-technical stakeholders.

• Ability to operate effectively in fast-paced environments and adapt proactively to changing priorities.

Preferred Qualifications

• Master’s degree or Ph.D. in Computer Science, Engineering, Mathematics, Statistics, or a related field.

• Experience working with multimodal models spanning text, image, and video.

• Experience with retrieval systems, vector databases, semantic search, and retrieval-augmented generation (RAG).

• Hands-on experience fine-tuning and optimizing open-source foundation models for downstream applications.

• Familiarity with distributed training and inference workflows for large-scale models.

• Understanding of GPU infrastructure, hardware optimization, and performance trade-offs in large-scale model training and inference.

• Familiarity with advanced LLM techniques such as RLHF, parameter-efficient tuning, and model adaptation methods.

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