Principal Data Scientist - Natural Language Processing, Applied AI

About the role

We'rebuilding the systems that turn one of the world's largest scientific corpora 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 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.

Whatyou'lldo

• 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.

Whatyou'llbring

• 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.

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 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.

Why us

We publish some of the world's most-read research, andwe'renow 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 theycame for.That'sthe work:from knowledge to impact.