Data Scientist

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🕒 June 8

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Pearson VUE

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1994

📚 Education

🛍️ eCommerce

☁️ SaaS

Education • eCommerce • SaaS

Pearson VUE is a global leader in computer-based testing, providing a wide range of credentialing and certification exams for various industries. They support test-takers and test owners by offering resources, scheduling options, and accommodations to ensure equitable access to testing. Their mission is to empower candidates and enrich communities through the delivery of high-stakes exams that validate professional skills and knowledge, contributing to career advancement and industry standards.

📋 Description

• We’re hiring a senior data scientist to help stand up and scale a shared data science capability that partners with stream-aligned teams. • You’ll report into the Data Science Team Manager and lead end‑to‑end DS/ML projects, shape standards, mentor teammates, and ship models into production, balancing quick wins with robust engineering. • In particular, we are currently exploring ideas around using AI and OCR to process documents and learner work, and to validate marking consistency in a range of qualifications. • Partner with stakeholders across the business to explore high‑impact opportunities. • Own the full lifecycle: problem framing, data discovery, feature engineering, modelling, evaluation, deployment, monitoring, and iteration. • Build and productionize LLM features where appropriate (retrieval‑augmented generation, evaluation, safety guardrails, cost/latency optimization) on AWS. • Contribute to DS/ML standards: experimentation, model governance, documentation, and reproducibility. • Mentor junior scientists, work with external contractors and collaborate closely with data engineering on pipelines and data quality.

🎯 Requirements

• A proven track record delivering projects in a Data Science or AI • Experience deploying models to production,understanding of deployment options and trade‑offs. • Practical LLM experience: prompting, fine‑tuning or adapter methods, and building RAG systems. • Orchestration: for example LangChain for pipelines/agents. • RAG best practices and evaluation workflows (e.g., agentic/RAG patterns on SageMaker). • Comfortable choosing the right technique for the job (from baselines to advanced models), with an emphasis on measurable impact and maintainability. • Clear communication with non‑technical partners; ability to translate outcomes to business metrics. • Strong Python for data science and ML; fluency with SQL. • A degree in a relevant discipline, ideally with further post graduate qualification. • Right to work in the UK

🏖️ Benefits

• Purpose-driven, learner-first; we prize curiosity, decency, and accountability, and we work to ensure everyone belongs and can grow their career. • Collaboration and asynchronous communication are essential.