Senior Data Scientist; NLP & Applied AI

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.

We’rebuilding the systems that turn one of the world’s largest scientific corpora intoresearchintelligence. That means productionNLP pipelines running over millions of journal articles, extracting entities, classifications, claim tuples, and summariesoptimizedfor use by downstream agentic applications.

We’relooking for a senior 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.

• 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-based approaches, 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.
• 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, and translate stakeholderpushbackinto concrete modeling work.

• Strong NLP background across modern (LLMs, transformers, embeddings, retrieval) and classical (NER, classification, sequence labeling) approaches.
  
  You’vebuilt evaluations and learned from the results.
• Cleanpython.
  
  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, andknow what to do when performance drifts.

• Experience working with scientific or scholarly text.
• Familiarity with AWS (S3, Batch, Lambda, Sage Maker) 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.

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…