Lead Decision, AI Scientist

MPB

. Identify and deliver high-value internal AI use cases .

Posted 10/15/2026full-timeBrighton • United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data science and applied AI, with a strong focus on building and evaluating AI prototypes, implementing evaluation engineering, and coaching teams on responsible AI innovation. Proficient in Python and modern AI toolkits, with experience in cloud data platforms and the ability to communicate findings effectively to executive audiences.

Highest-signal resume keywords

Data ScienceMachine LearningPython ProgrammingEvaluation EngineeringCloud Data Experience

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Hard Skills

Data ScienceMachine LearningPython ProgrammingEvaluation EngineeringAgentic WorkflowsMulti-Agent SystemsProduction LLM ApplicationsEvaluation SetsScorecardsRegression Tests

Soft Skills

CoachingCommunication

Tools & Technologies

BigQueryVertex AILLM FrameworksVector StoresEvaluation Tooling

Industry Keywords

AI Use CasesInternal AutomationTrustworthy AI SystemsMarketplaceE-Commerce

Tech Stack

Tools & technologies

BigQueryCloudPython

About the role

Key responsibilities & impact

Identify and deliver high-value internal AI use cases
Turn ideas such as agentic workflows or internal automation into focused, time-boxed experiments
Build, evaluate and demonstrate working prototypes
Create evaluation sets, scorecards and guardrails for trustworthy AI systems
Build the data foundations and retrieval underpinning AI capabilities
Work as a peer with the Lead AI Product Manager and Lead AI Automation Engineer in a shared-ownership, peer-led Hub
Recommend whether experiments should be stopped, expanded or handed off
Coach teams across MPB to innovate with AI responsibly
Monitor emerging AI developments and contribute to MPB's internal AI centre of excellence

Requirements

What you’ll need

Substantial hands-on experience in data science, ML or applied AI
Experience with agentic workflows, multi-agent systems or production LLM applications
Evaluation engineering, including eval sets, scorecards, regression tests and guardrails for non-deterministic systems
Strong, production-quality Python and the modern AI toolkit, including LLM frameworks, vector stores and evaluation tooling
Cloud data experience, including BigQuery and at least one production ML or AI platform such as Vertex AI
Ability to explain findings clearly to exec-level audiences and coach non-technical teams
Nice to have: computer vision, multi-modal or retrieval experience
Nice to have: experience in marketplace, e-commerce or content-rich product environments
Nice to have: open-source contributions or published applied work
A Master's or PhD is nice to have, but demonstrated craft is valued over credentials

Benefits

Comp & perks

Build something new: help define a brand-new function, with real ownership and real impact
Interesting data: kit imagery, content and search provide real problems to solve in a business that's good for people and the planet
Low-hierarchy, high-trust, peer-led Hub where ideas are heard
Full-time, permanent employment
Hybrid working arrangement