This role suits an experienced data science leader who wants to build a machine learning function from the ground up in the UK. This is a founding position at Paddle, where the person will personally deliver the first systems into production before hiring and leading a team.
Role Overview
Paddle is hiring a Head of Data Science in the UK to establish the data science capability from the ground up. The position owns automated decision-making inside the product, spanning traditional machine learning and agentic systems rather than decision-support analytics. Initially, the role is hands-on, involving feature engineering, model training, deployment, and operation. Once initial use cases prove successful, the individual will hire and lead a hub-and-spoke team embedded across high-value business areas, reporting directly to the VP of Data.
Key Responsibilities
Prioritise high-impact opportunities by building and refreshing a value-based use-case backlog across payment performance, revenue recovery, sales and marketing operations, growth and monetisation intelligence, and risk, trust and compliance.
Personally deliver the first system end to end, covering analysis, back-testing, feature engineering, model or agent creation, deployment, and shadow or A/B testing.
Operate deployed systems by owning monitoring, retraining, drift response, incident handling, and rollbacks alongside engineering teams.
Work across traditional machine learning and agentic systems, selecting the appropriate approach such as propensity models, uplift models, rules, or tool-using agents.
Build and lead a team by hiring senior data scientists and machine learning engineers, and establishing shared methods and reusable components.
Establish the production stack with Data Platform and Engineering, including reproducible training pipelines, model registries, inference services, feature stores, and observability.
Own end-to-end value capture by shadow-testing and A/B testing deployments, translating metric movement into financial figures co-signed by Finance, and publishing quarterly reports.
Set the governance model for automated decision-making, addressing risk assessment, latency requirements, human escalation, rollbacks, and compliance obligations such as GDPR and the EU AI Act.
Define functional boundaries with Product Science, Analytics Engineering, Data Platform, and AI Enablement.
Deliver cross-functionally by embedding directly in delivery groups alongside product owners, domain experts, and platform engineers.
Requirements
Experience leading data science or machine learning teams that own systems in production with measurable commercial impact.
Hands-on current technical capability in writing SQL and Python, engineering features, and evaluating models and agents.
Experience across both traditional machine learning and agentic systems, understanding feature pipelines, drift, tool design, prompt iteration, and trace observability.
Practice in running live systems, including monitoring, retraining, incident response, rollbacks, and on-call responsibilities.
Pragmatic approach to methods, using rules or heuristics where appropriate while reserving models and agents for high-value problems.
Comfort with an undefined starting point, shaping an unestablished roadmap and defending it to executive audiences.
Strong capabilities in measurement, experimentation, uplift modelling, and back-testing.
Fluency in the engineering side of production machine learning and agents, including training pipelines, model registries, inference services, and drift detection.
Ability to hire, level, and develop senior data scientists and machine learning engineers.
Credibility with executives and commercial stakeholders to translate business problems into value estimates and manage prioritisation.
Experience in payments, fintech, subscriptions, high-volume commercial operations, or another regulated transactional domain.
Familiarity with treating regulated machine learning as a design constraint, including model risk assessments, DPIAs, auditability, and traceability.
Skills and Tools
SQL
Python
Machine Learning
Agentic Systems
A/B Testing
Uplift Modelling
Feature Stores
Model Registries
Inference Services
Trace Observability
About the Company
Paddle offers digital product companies a Merchant of Record approach to payment infrastructure, removing payment fragmentation for over 6,000 software sellers across 245 territories globally. The company is backed by investors including KKR, FTV Capital, Kindred, Notion, and 83North.
How to Apply
Candidates should apply directly on the official company careers page.
