Senior Data Scientist (NLP & Applied AI)

Wiley•Hoboken, NJ

3d•$109,500 - $156,833•Remote

About The Position

We are building the systems that turn one of the world's largest scientific corpora into research intelligence. This involves production NLP pipelines processing millions of journal articles to extract entities, classifications, claim tuples, and summaries optimized for downstream agentic applications. We are seeking a senior data scientist to take end-to-end ownership of domain-specific content modeling, from evaluation sets to production deployment. You will join a small, senior team where data scientists are responsible for their models in production. This includes writing code, owning evaluations, deploying changes, and being accountable for outcomes. This is a hands-on role for an individual who desires to see their models utilized by real users in a dynamic market.

Requirements

Strong NLP background encompassing modern approaches (LLMs, transformers, embeddings, retrieval) and classical techniques (NER, classification, sequence labeling).
Proven experience in building and learning from NLP evaluations.
Proficient Python coding skills, comfortable in both exploratory notebooks and production repositories.
A demonstrated habit of comparing different approaches and selecting the most suitable one for a given task, with the ability to justify choices with evaluations and cost estimates, and to address performance drift.
Experience in production NLP pipelines.

Nice To Haves

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.
Exposure to agentic AI applications, including tool use, multi-step reasoning, guardrails, and evaluation of trajectories.

Responsibilities

Design and build NLP enrichment pipelines to extract entities, classifications, claims, and summaries from scientific full-text at scale.
Compare NLP approaches (traditional, embedding-based retrieval, LLM prompting, fine-tuned smaller models) against LLM-based approaches for extraction and enrichment, selecting the optimal tool for each task based on evaluation, cost, and operational tradeoffs.
Own evaluation processes, including building golden sets in consultation with SMEs and vendors, selecting appropriate metrics, and making informed tradeoffs between speed, quality, and cost.
Contribute to agentic AI application development, specifically in tool-using systems that reason over the enriched corpus, leveraging NLP and evaluation expertise to shape agent grounding and answer defense.
Collaborate directly with editors, product managers, and engineers, integrating the modeling perspective into product decisions and translating stakeholder feedback into actionable modeling work.