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're building the systems that turn one of the world's largest scientific corpora into research intelligence. That means production NLP pipelines running over millions of journal articles, extracting entities, classifications, claim tuples, and summaries optimized for use by downstream agentic applications. We're looking for a senior data scientist to own domain-specific content modeling work end to end, from the eval set through the pipeline stage that ships it.
You'll join a small, senior team where data scientists own their models in production. You'll write 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 users in a rapidly evolving market.
Job 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.
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.
Required Qualifications:
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.
Clean python. You are comfortable in exploratory notebooks and production repositories, and an engineer taking over a modeling output from you has a good head start.
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.
Preferred Qualifications:
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.
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:
109,500.00 USD to 156,833.33 USD #LI-CW1Skills Required
- Strong NLP background across LLMs, transformers, embeddings, retrieval, NER, classification, and sequence labeling, including building evaluations.
- Strong Python programming skills for exploratory notebooks and production repositories.
- Ability to compare modeling approaches, select appropriate methods, support decisions with evaluations and cost estimates, and respond to performance drift.
- Experience working with scientific or scholarly text.
- Familiarity with AWS services including S3, Batch, Lambda, and SageMaker.
- Familiarity with Parquet or Iceberg data lake patterns.
- Experience running LLMs under production cost and latency budgets.
- Exposure to agentic AI applications, including tool use, multi-step reasoning, guardrails, and trajectory evaluation.
What We Do
Learning House was founded in 2001 to aid small independent schools in building distance learning programs that serve the needs of
