Principal Data Scientist

Wiley Global Technology•Hoboken, NJ

About The Position

We are building systems that transform one of the world's largest scientific corporations 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 principal data scientist to take end-to-end ownership of domain-specific content modeling, from evaluation sets to production deployment. This is a hands-on role within a small, senior team where data scientists are responsible for their models in production, including writing code, owning evaluations, shipping changes, and being accountable for outcomes. It's an opportunity for someone who wants to see their models used by real users in a dynamic market.

Requirements

Deep Python expertise, with years of experience writing it in production at scale.
Understanding of concurrency models (asyncio vs. threads vs. queues) and their tradeoffs.
Strong NLP background encompassing modern approaches (LLMs, transformers, embeddings, retrieval) and classical methods (NER, classification, sequence labeling).
Proven experience building and learning from NLP evaluations.
Habit of comparing approaches and selecting the most suitable one for a given task, with the ability to justify choices with evaluation data and cost estimates.
A track record of shipping production systems that deliver value to users, not just prototypes or papers.

Nice To Haves

Experience working with scientific or scholarly text.
Familiarity with AWS services (S3, Batch, Lambda, SageMaker).
Experience with Parquet or Iceberg data lake patterns.
Experience running LLMs in production under real cost and latency budgets.
Some 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 metrics, and making productive tradeoffs between speed, quality, and cost.
Write production-quality Python code, managing concurrency and cost for high-volume LLM workloads.
Structure code for engineerability and extensibility by other data scientists.
Collaborate with data engineers to orchestrate work using data pipeline and data build tools like Airflow and Dagster.
Design idempotent, retryable, and evaluable pipeline stages that maintain reliability at scale.
Contribute to agentic AI application development, specifically in tool-using systems that reason over the enriched corpus, leveraging NLP and evaluation expertise to shape how agents ground and defend answers.
Work directly with editors, product managers, and engineers, bringing a modeling perspective to product decisions and translating stakeholder feedback into concrete modeling work.