Lead Data Scientist, AI Labs New Washington, District of Columbia, United States

Washington, District of Columbia, United States

A thriving, mission-driven multimedia organization, NPR produces award-winning news, information, and music programming in partnership with hundreds of independent public radio stations across the nation. The NPR audience values information, creativity, curiosity, and social responsibility – and our employees do too. We are innovators and leaders in diverse fields, from journalism and digital media to IT and development. Every day, our employees and member stations touch the lives of millions worldwide.

Across our organization, we’re building a workplace where collaboration is essential, diverse voices are heard, and inclusion is the key to our success. We are committed to doing the right thing in our journalism and in every role s means that integrity, adherence to our ethical standards, and compliance with legal obligations are fundamental responsibilities for every employee at NPR.

As the Lead Data Scientist for the AI Labs team, you will serve as the technical and ethical anchor for NPR's artificial intelligence initiatives. You will lead data science expertise for a content metadata overhaul to power new audience-focused personalization engines. Rather than building models from scratch, you will focus on fine-tuning, evaluating, and scaling existing foundational models, as well as deploying machine learning algorithms for public media..

You will collaborate extensively across the organization to align architectures, ensure secure cloud deployments, and protect intellectual property. This position demands a high focus on accuracy, journalistic ethics, data privacy, and responsible scaling.

• Lead the selection, fine-tuning, and optimization of open-source and proprietary LLMs tailored to NPR’s unique content voice.
• Design and architect automated machine learning pipelines to transform decades of unstructured audio, transcripts, and text to support automated semantic metadata generation.
• Collaborate with the product, design and engineering teammates to translate user needs into production-ready data science workflows.
• Architect recommendation frameworks that leverage enriched metadata to drive deep, style-based audience personalization while preserving editorial curation.
• Partner with Data Products to construct clean, self-service data pipelines and audience analytics models inside Big Query.
• Support newsroom research through the prototyping, validation, and development of API-driven tooling and secure database search models.
• Establish strict evaluation, testing, and benchmarking frameworks to guarantee model outputs meet NPR’s standards for factual accuracy and neutrality.
• Proactively identify, audit, and mitigate algorithmic bias in metadata generation and audience discovery systems.
• Ensure all AI applications scale securely and cost-effectively, balancing computational efficiency with rigorous data privacy guardrails.
• Collaborate with growth and data platforms to leverage content metadata for user lifecycle retention and smart audience segmentation.

The above duties and responsibilities are not an exhaustive list of required responsibilities, duties and skills. Other duties may be assigned, and this job description is subject to change at any time.

• 8+ years of professional experience in Data Science, Machine Learning, or Natural Language Processing (NLP) shipping production-grade systems.
• Proven track record of applying, fine-tuning, and evaluating Large Language Models (LLMs) and foundational architectures.
• Experience with Automatic Speech Recognition (ASR), diarization, and audio preprocessing pipelines.
• Demonstrated experience designing and maintaining large-scale data architectures, vector databases, and semantic search pipelines.
• Hands-on experience designing, deploying, and optimizing production-grade recommendation engines or personalization systems at scale.
• Practical, hands-on experience building machine learning workflows within major cloud environments (Azure, AWS or GCP).
• Experience successfully navigating matrixed, cross-disciplinary collaboration between technical engineering teams and…