[Remote] Senior Data Scientist (NLP & Applied AI)

Note: The job is a remote job and is open to candidates in USA. Wiley is building production NLP systems that transform its scientific corpus into research intelligence. The Senior Data Scientist will own domain-specific content modeling end to end, including NLP pipelines, evaluation, and production delivery, and will contribute to agentic AI applications.

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

Skills

• 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
• 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
• 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.
• Meeting-free Friday afternoons, allowing more time for focused work and professional development.
• Continual learning and internal mobility.
• Employee programs offering opportunities to foster community, learn, and grow.

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