Principal Data Scientist, NLP, Applied AI

Wiley

. Design and build NLP enrichment pipelines extracting entities, classifications, claims, and summaries from scientific full-text at scale .

Posted 9/17/2026full-timeRemote • United KingdomLead💰 £59,100 - £84,633 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and building NLP enrichment pipelines, with a strong focus on production-quality Python development and the ability to evaluate and select appropriate modeling approaches based on operational tradeoffs. Proficient in collaborating with cross-functional teams to drive product decisions and outcomes in agentic AI applications.

Highest-signal resume keywords

Production Python ExperienceNatural Language Processing (NLP)Large Language Models (LLMs)Data Pipeline OrchestrationEvaluation Metrics Selection

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonNLPLLMsTransformersEmbeddingsNERClassificationSequence LabelingConcurrency ManagementProduction Systems

Soft Skills

CollaborationStakeholder CommunicationJustification of Choices

Tools & Technologies

AirflowDagsterAWS S3AWS LambdaAWS SageMakerParquetIceberg

Industry Keywords

Scientific TextAgentic AIEvaluation SetsOperational TradeoffsHigh-Volume Workloads

Tech Stack

Tools & technologies

AirflowAWSPython

About the role

Key responsibilities & impact

Design and build NLP enrichment pipelines extracting entities, classifications, claims, and summaries from scientific full-text at scale
Compare traditional NLP, embedding-based retrieval, LLM prompting, and fine-tuned smaller models, selecting approaches based on evaluation, cost, and operational tradeoffs
Build golden evaluation sets with subject-matter experts and vendors
Choose metrics and balance speed, quality, and cost
Write production-quality Python and manage concurrency and costs for high-volume LLM workloads
Collaborate with data engineers to orchestrate work in Airflow, Dagster, and data build tools
Design idempotent, retryable, and evaluable pipeline stages
Contribute to agentic AI applications that reason over enriched scientific corpora
Collaborate with editors, product managers, and engineers to inform product decisions and translate stakeholder feedback into modeling work
Own models, evaluations, code, production deployments, and outcomes end to end

Requirements

What you’ll need

Deep production Python experience at scale, including asyncio, threads, and queues
Strong NLP background across LLMs, transformers, embeddings, retrieval, NER, classification, and sequence labeling
Experience building evaluations and learning from results
Ability to compare approaches and justify choices using evaluation, cost, and operational tradeoffs
Track record of shipping production systems that deliver value to real users
Experience working with scientific or scholarly text (nice to have)
Familiarity with AWS S3, Batch, Lambda, SageMaker, and Parquet or Iceberg data lake patterns (nice to have)
Experience running LLMs under production cost and latency budgets (nice to have)
Exposure to agentic AI applications, tool use, multi-step reasoning, guardrails, and trajectory evaluation (nice to have)
Resume/CV required for application

Benefits

Comp & perks

Meeting-free Friday afternoons
Professional development opportunities
Employee programs supporting community, learning, and growth
Comprehensive benefits package
Competitive compensation
Reasonable accommodation for applicants and employees with disabilities
Internal mobility opportunities