Principal Data Scientist

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.

We're building the systems that turn one of the world's largest scientific corporation 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 principal 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.

Design and build NLP enrichment pipelines that extract entities, classifications, claims, and summaries from scientific full‑text pare 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 tribute 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.

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.

Preferred:
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 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…