Principal Data Scientist

Wiley; United States; Remote; USD 140,000 - 201,000; Full time

Job summary

Wiley is seeking a Principal Data Scientist to own domain-specific content modeling end to end, from eval sets through shipping pipelines. You will design NLP enrichment pipelines, compare traditional NLP with LLM-based approaches, and own evaluations and metrics in production for high-volume scholarly text.

You will collaborate with editors, product managers, and engineers, writing scalable Python and building robust data pipelines using Airflow/Dagster to support agentic AI applications while

Qualifications

• Deep Python expertise with production-grade, scalable code.
• Strong NLP background across modern (LLMs, transformers, embeddings, retrieval) and classical approaches.

Responsibilities

• Design and build NLP enrichment pipelines that extract entities, classifications, claims, and summaries from scientific full-text at scale.
• Compare NLP approaches to extraction and enrichment against LLM-based approaches, and pick the right tool for each task with evaluation, cost, and operational tradeoffs.
• Own evaluation. Build golden sets with SMEs and vendors, choose metrics, and balance speed, quality, and cost.
• Write production-quality Python. Manage concurrency and cost for high-volume LLM workloads; craft extendable code.
• Collaborate with data engineers to orchestrate work in data pipelines and tools like Airflow and Dagster; design idempotent, retryable stages.
• Contribute to agentic AI applications: tool use, multi-step reasoning, guardrails, evaluation of trajectories.
• Work with editors, product managers, and engineers; translate stakeholder input into modeling work.

Skills

Deep Python

LLMs

Transformers

Embeddings

Retrieval

NER

Classification

Sequence labeling

LLMs in production

Agentic AI concepts

Scientific text

Tools

AWS S3

AWS Batch

AWS Lambda

AWS SageMaker

Parquet

Iceberg

Job description

Job Description:

We believe in bold ideas, diverse perspectives, and the drive to transform knowledge into impact. Here, your curiosity fuels progress, your voice shapes innovation, and your ambition helps redefine what's possible within science and learning. We are a culture that obsesses over impact, challenges, and drives what's next to power infinite possibilities for our customers, colleagues and society at large.

About the Role:

We'rebuilding the systems that turn one of the world's largest scientific corporation intoresearchintelligence. That meansproductionNLP pipelines running over millions of journal articles, extracting entities, classifications, claim tuples, and summariesoptimizedfor use by downstream agentic applications.We'relooking for a principal data scientist to owndomain-specificcontentmodeling work end to end, from the eval set through the pipeline stage that ships it.

You'lljoin a small, senior team where data scientists own their models in production.You'llwrite the code, own the evaluations, ship the changes, and stay accountable for the outcomes. This is a hands-on role for someone who wants to see their models through to real usersin a rapidly evolving market.

Job Responsibilities:

• Design and build NLP enrichment pipelines that extract entities, classifications, claims, and summaries from scientificfull-textat scale.

• Compare NLP approaches to extraction and enrichment against LLM-basedapproaches, andpick the right tool for each task. That means putting traditional NLP (NER, sequence labeling, classification), embedding-based retrieval, LLM prompting, and fine-tuned smaller models on the same table, and defending each choice with evaluation, cost, and operational tradeoffs. This is a core part of the job, not an occasional exercise.

• Own evaluation. Build the golden setsin consultation with SMEs and vendors, choose the metrics, and make productive tradeoffs between speed, quality, and cost.

• Write production-quality Python. Manage concurrency and cost for high-volume LLM workloads. Structure code that engineers canshipand other data scientists can extend.

• Collaborate with a team of data engineers to orchestrate work in datapipelineand data build tools like Airflow andDagster. Design idempotent,retryable, evaluable pipeline stages that stay reliable when a run fails at scale.

• Contribute to agentic AI application work: tool-using systems that reason over the enriched corpus, where your NLP and evaluation background will shape how the agent grounds and defends its answers.

• Work directly with editors, product managers, and engineers. Bring the modeling perspective into productdecisions, andtranslate stakeholderpushbackinto concrete modeling work.

Job Requirements:

• Deep Python.You'vewritten it in production, at scale, for years. You know when to reach forasyncioversus threads versus a queue, and you can explain the tradeoff clearly.

• Strong NLP background across modern (LLMs, transformers, embeddings, retrieval) and classical (NER, classification, sequence labeling) approaches.You'vebuilt evaluationsand learned from the results.

• A habit of comparing approaches and choosing the right one for the task. You can defend "prompt a large LLM" and "train a small classifier on 2,000 labels" with equal seriousness, back the choice with an eval and a costestimate, andknow what to do when performance drifts.

• A track recordof shipping - not just prototypes and papers, but systems that deliver value to real users.

Preferred:

• Experience working with scientific or scholarly text.

• Familiarity with AWS (S3, Batch, Lambda, SageMaker) and Parquet or Iceberg data lake patterns.

• Experience running LLMs underreal costand latency budgets in production.

• Some exposure to agentic AI applications: tool use, multi-step reasoning, guardrails, and evaluation of trajectories rather than single-turn outputs.

We power infinite possibilities.

For more than 200 years, we've transformed knowledge into discoveries that shape the world. Today, our global team of innovators, creators, and experts is driving what's next in science, education, and publishingâcreating impact that reaches everywhere.

We're not just observers of progress. We're the ones accelerating scientific breakthroughs, advancing learning, and sparking innovation that redefines entire fields and improves lives.

Here, your talent matters. Your ideas have room to grow. And your work creates breakthroughs that can change everything.

Wiley is an equal opportunity/affirmative action employer. We evaluate all qualified applicants and treat all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, protected veteran status, genetic information, or based on any individual's status in any group or class protected by applicable federal, state or local laws. Wiley is also committed to providing reasonable accommodation to applicants and employees with disabilities. Applicants who require accommodation to participate in the job application process may contact [e-mail] for assistance.

We are proud that our workplace promotes continual learning and internal mobility. We offer meeting-free Friday afternoons allowing more time for heads down work and professional development, and through a robust body of employee programing we facilitate a wide range of opportunities to foster community, learn, and grow.

We are committed to fair, transparent pay, and we strive to provide competitive compensation in addition to a comprehensive benefits package. The range below represents Wiley's good faith and reasonable estimate of the base pay for this role at the time of posting roles in the United Kingdom, Canada, USA, Austria, Czechia, Denmark, France, Greece, Italy, Netherlands, Romania, or Spain. It is anticipated that most qualified candidates will fall within the range, however the ultimate salary offered for this role may be higher or lower and will be set based on a variety of non-discriminatory factors, including but not limited to, geographic location, skills, and competencies.

Salary Range:

140,000.00 USD to 200,733.33 USD#LI-JG1