Lead Data Scientist, AI Labs

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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 BigQuery.
• →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.

• 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 non-technical stakeholders.
• Ability to identify common AI pitfalls, such as hallucinations or formatting errors, and design robust algorithmic workarounds.

• Experience working with large volumes of unstructured multimedia, digital audio processing, or automated speech-to-text workflows.
• Prior experience working within a media organization, digital newsroom, or public service institution.
• Experience integrating automated LLM evaluation gates and regression testing directly into CI/CD deployment pipelines

• Advanced degree (PhD preferred) in Computer Science, Data Science or Machine Learning
• Advanced academic research in relevant fields

• Hybrid Permitted: 
  • This is a hybrid permitted role. Some aspects of this role require duties better performed at an NPR facility. The employee will be required to be in the office at the [Washington, D.C. or New York City] location at least two to three days per week.

• Deep technical expertise in Python, ML and agentic engineering frameworks, vector search engines and two-stage retrieval architectures.
• Strong proficiency in SQL and cloud data warehouse ecosystems (such as BigQuery or Snowflake).
• Deep understanding of Retrieval-Augmented Generation (RAG) patterns and semantic search tools.
• Professional-level familiarity with model evaluation methodologies, prompt engineering techniques, and API integration workflows.
• Strong commitment to algorithmic ethics, data privacy compliance, and methods for identifying or mitigating bias.

• This is a full-time, exempt position.

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