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About This Role
OVERVIEW
*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 at NPR**.* *This means that integrity, adherence to our ethical standards, and compliance with legal obligations are fundamental responsibilities for every employee at NPR.*
### Intro to Position
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.
### Responsibilities
- 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.
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.
### Minimum Qualifications
- 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.
### Preferred Qualifications
- 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
### Required Skills/Competencies
- 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.
### Education Requirements
- Advanced degree (PhD preferred) in Computer Science, Data Science or Machine Learning
- Advanced academic research in relevant fields
### Work Location \& Requirements
- 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.
### Job Type
- This is a full\-time, exempt position.
### Compensation
Salary Range: The U.S.\-based anticipated salary range for this opportunity is $164,000 – $201,000 plus benefits. The range displayed reflects the minimum and maximum salaries NPR expects to provide for new hires for the position across all US locations.
NPR Benefits: NPR provides comprehensive benefits for employees and dependents. Regular, full\-time employees scheduled to work 30 hours or more per week are eligible to enroll in NPR's benefits options. Benefits include access to health and wellness, paid time off, and financial well\-being. Plan options include medical, dental, vision, life/ accidental death and dismemberment, long\-term disability, short\-term disability, and voluntary retirement savings to all eligible NPR employees.
Does this sound like you? If so, we want to hear from you.
\#LI\-HYBRID
NPR is an Equal Opportunity Employer. NPR is committed to being an inclusive workplace that welcomes diverse and unique perspectives, all working toward the same goal – to create a more informed public. Qualified applicants receive consideration for employment without regard to race, color, ethnicity, national origin, ancestry, age, religion, religious belief, sex (including pregnancy, childbirth and related medical conditions, lactation, and reproductive health decisions), sexual orientation, gender, gender identity or expression, transgender status, gender non\-conforming status, intersex status, sexual stereotypes, nationality, citizenship status, personal appearance, marital status, family status, family responsibilities, military status, veteran status, mental and physical disability, medical condition, genetic information, genetic characteristics of yourself or a family member, political views and affiliation, unemployment status, protective order status, status as a victim of domestic violence, sexual assault, or stalking, or any other basis prohibited under applicable law.
If you are a person with a disability needing assistance with the application process, please reach out to [email protected].
You may read NPR's privacy policy to learn about how NPR may handle information you submit with any application.
Want more NPR? Explore the stories behind the stories on our NPR Extra blog. Get social with NPR Extra on Facebook and Instagram. Find more career opportunities at NPR.org/careers.
Salary Context
This $164K-$201K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At NPR, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($182K) sits 5% below the category median. Disclosed range: $164K to $201K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
NPR AI Hiring
NPR has 1 open AI role right now. They're hiring across Data Scientist. Based in Washington, DC, US. Compensation range: $201K - $201K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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