Staff, Data Science & Applied AI

Description

The company is a global media and entertainment business with three segments: Studios, Direct-to-Consumer and TV Media.

This is a senior individual contributor role on the Enterprise Data and AI Solutions team, which supports the company's studios, streaming, linear networks, consumer products, games and direct-to-consumer platforms.

You will be a hands-on applied data scientist who combines statistical depth, machine learning engineering and measurable business outcomes.

You will turn hard business problems into scalable analytics, production models and data products, and work alongside Product, Engineering and business teams to build and scale advanced analytics and AI across the organization.

Responsibilities

• Build statistical, predictive and machine learning models for customer analytics, forecasting, personalization, optimization and content performance, then deploy them.
• Apply ensemble methods, gradient boosting, deep learning, NLP, time-series forecasting and recommendation systems where they fit the problem.
• Validate models rigorously and track their performance and robustness over time.
• Design and build generative AI solutions on large language models for knowledge retrieval, content intelligence, metadata enrichment, summarization and workflow automation.
• Create and refine prompt strategies, evaluation frameworks and guardrails so outputs stay accurate and dependable.
• Architect retrieval-augmented generation pipelines that combine structured and unstructured enterprise data.
• Fine-tune or adapt foundation models with parameter-efficient methods such as LoRA and adapters, when the business case supports it.
• Build evaluation pipelines that measure hallucination, bias, latency, cost and output quality in production.
• Work with Responsible AI and Governance teams to meet enterprise AI policies, data privacy standards and ethical AI practices.
• Work with Data Engineering and DevOps to move ML and GenAI solutions into scalable cloud production environments.
• Design CI/CD pipelines covering experiment tracking, versioning and automated retraining across the model lifecycle.
• Set up monitoring for model drift, prompt drift, performance decline and data integrity.
• Develop reusable ML and GenAI frameworks, accelerators and internal tools that help other teams work faster.
• Automate manual workflows to speed up analysis.
• Keep up with progress in foundation models, multimodal AI and agentic architectures, and bring useful advances into the company's AI capabilities.

Requirements

• A bachelor's degree or higher, such as an MS, in computer science, data science, engineering, mathematics, statistics or a related quantitative field.
• At least 8 years of data science experience, including at least 2 years working with generative AI.
• A proven record of delivering production AI and ML solutions with measurable business impact.
• Hands-on experience designing and deploying generative AI solutions on large language models, such as GPT-class models, open-source foundation models or enterprise LLM platforms.
• Strong skill in prompt engineering, structured output design, few-shot learning and systematic prompt optimization.
• Experience building retrieval-augmented generation pipelines that connect vector databases with enterprise data.
• Familiarity with embedding models, semantic search and vector stores such as Pinecone, Weaviate, OpenSearch, FAISS or similar.
• Experience fine-tuning or adapting foundation models with parameter-efficient methods such as LoRA, adapters or instruction tuning.
• Understanding of LLM evaluation, including hallucination detection, bias assessment, response quality scoring and cost-performance trade-offs.
• Experience with enterprise AI platforms such as AWS Bedrock, Azure OpenAI, Databricks Model Serving, Snowflake Cortex or similar.
• Understanding of Responsible AI principles, data privacy and model governance in regulated environments.
• Nice to have: exposure to multimodal AI across text, image, audio and video, and to agent-based workflows.

Conditions

Qualified candidates with disabilities can request adjustments or accommodations during recruitment.