Advanced Specialist, Data Scientist

• 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

Experience in one or more of our domains (assessment/psychometrics, workforce skills/ontologies, recommendations, fraud detection).

Familiarity with MLOps practices (CI/CD for ML, experiment tracking, data/version control) in a cloud environment.

Senior Data Scientist - Enterprise Learning & Skills (ELS), Pearson

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.

Tech focus: Python and AWS (or equivalents in Azure or GCP), with hands‑on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock.

• 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.

Purpose‑driven, learner‑first; we prize curiosity, decency, and accountability, and we work to ensure everyone belongs and can grow their career.

ELS roles span multiple geographies and partner teams; collaboration and asynchronous communication are essential.

This is a hybrid role, located in Central London, with an expectation of 1-2 days in the office each week.