Note: The job is a remote job and is open to candidates in USA. Wiley is building research intelligence systems from scientific content. The Principal Data Scientist will own domain-specific content modeling end to end, building and evaluating production NLP and applied AI pipelines and collaborating with engineers and product stakeholders to deliver them to users.
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
Skills
- 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
Benefits
- Remote work arrangement.
- Meeting-free Friday afternoons, allowing more time for focused work and professional development.
- Internal mobility and opportunities to learn and grow through employee programs.
- A comprehensive benefits package.
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