Advanced Specialist, Data Scientist

Pearson VUE

Senior Data Scientist leading end-to-end DS/ML projects at Pearson's Enterprise Learning & Skills. Collaborating across teams to enhance skills development through innovative data science applications.

Posted 7/8/2026full-timeLondon • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Science and AI, with a focus on deploying models to production and building LLM features. Strong ability to mentor junior scientists and collaborate with stakeholders to drive measurable business impact.

Highest-signal resume keywords

Data Science Project DeliveryLLM ExperienceModel DeploymentPython for Data ScienceSQL Fluency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data DiscoveryFeature EngineeringModel EvaluationModel GovernanceExperimentationRAG SystemsCost OptimizationLatency OptimizationDeployment TechniquesOrchestration

Soft Skills

Clear CommunicationMentoring

Tools & Technologies

AWSLangChainSageMaker

Industry Keywords

Data ScienceAIMachine LearningModel ReproducibilityBusiness Metrics

Tech Stack

Tools & technologies

AWSPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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