Principal Data Scientist (NLP + Applied AI)

About the role

Nice to have

• Experience working with scientific or scholarly text.
• Familiarity with AWS (S3, Batch, Lambda, SageMaker) and Parquet or Iceberg data lake patterns.
• Experience running LLMs under real cost and 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.
• Why us
• We publish some of the world's most-read research, and we're now in a rare position: applying modern AI to a corpus of trusted scientific knowledge that spans two centuries. Researchers will use the systems you build here to move faster and get closer to the answers they came for. That's the work: from knowledge to impact.
• 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 publishing—creating 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 [email protected] 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. When applying, please attach your resume/CV to be considered.
• Salary Range:

Qualifications

• Deep Python. You've written it in production, at scale, for years. You know when to reach for asyncio versus 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've built evaluations and 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 cost estimate, and know what to do when performance drifts.
• A track record of shipping – not just prototypes and papers, but systems that deliver value to real users.
• Experience working with scientific or scholarly text.
• Familiarity with AWS (S3, Batch, Lambda, SageMaker) and Parquet or Iceberg data lake patterns.
• Experience running LLMs under real cost and 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.
• Why us
• We publish some of the world's most-read research, and we're now in a rare position: applying modern AI to a corpus of trusted scientific knowledge that spans two centuries. Researchers will use the systems you build here to move faster and get closer to the answers they came for. That's the work: from knowledge to impact.
• 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 publishing—creating 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 [email protected] 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. When applying, please attach your resume/CV to be considered.
• Salary Range

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. 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 sets in 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 can ship and other data scientists can extend.
• Collaborate with a team of data engineers to orchestrate work in data pipeline and data build tools like Airflow and Dagster. 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 product decisions, and translate stakeholder pushback into concrete modeling work.