Head of Data Science

This is a fully remote position, open to applicants in United Kingdom, +2 more countries.

📋 Description

• Develop Paddle's data science capabilities from the ground up.

• Identify and prioritize automated decision-making opportunities related to payment performance, revenue recovery, sales and marketing operations, growth and monetization intelligence, as well as risk, trust, and compliance.

• Lead the end-to-end delivery of the initial system, encompassing analysis, back-testing, feature engineering, model or agent development, deployment, shadow testing, and A/B testing.

• Manage deployed systems, taking ownership of monitoring, retraining, drift response, incident management, and rollback procedures.

• Assess whether each problem necessitates rules, traditional machine learning, or agent-based systems.

• Recruit and guide a hub-and-spoke team of data scientists and machine learning engineers.

• Establish the production stack in collaboration with Data Platform and Engineering, which includes training data, artifacts, model registry, inference services, historical features, evaluation harnesses, trace observability, and monitoring.

• Ensure value capture by measuring deployments against existing strategies, converting metric changes into financial value with the Finance team, and publishing quarterly reports.

• Set governance for automated decision-making in collaboration with Legal, Privacy, Compliance, and Risk teams, addressing GDPR, EU AI Act, and payment obligations.

• Define interfaces with Product Science, Analytics Engineering, Data Platform, and AI Enablement.

• Collaborate with Product, Payments, Engineering, Risk, and Finance, embedding within delivery groups.

• Report directly to the VP of Data.

⛳️ Requirements

• Proven experience leading data science or machine learning teams that manage systems in production.

• Practical experience in writing SQL and Python, engineering features, evaluating models and agents, and deploying systems.

• Familiarity with both traditional machine learning and agentic systems.

• Experience with propensity and uplift models, feature pipelines, drift management, tool and context design, prompt and retrieval iteration, evaluations against golden answer sets, and trace observability.

• Background in operating live systems, including monitoring, retraining, incident response, and rollback strategies.

• Experience with experimentation, uplift modeling, and back-testing methodologies.

• Proficiency in production machine learning and agent engineering, including training pipelines, model registries, inference services, feature stores, drift detection, and trace observability.

• Capability to recruit, assess, and develop senior data scientists and machine learning engineers.

• Experience in communicating effectively with executives and commercial stakeholders.

• Background in payments, fintech, subscriptions, high-volume commercial operations, or other regulated transactional domains.

• Experience with model risk assessment, Data Protection Impact Assessments (DPIAs), and ensuring model and policy version auditability and traceability.

• No specific educational credentials required; Paddle values skills over where candidates completed their studies.

• Must indicate whether visa sponsorship will be necessary.

🏝️ Benefits

• Unlimited holidays.

• Four months of paid family leave, regardless of gender.

• Options for remote work, office hub work, or a combination of both.

• Annual learning fund.

• Regular training opportunities, both internal and external.

• Support for personal development.

• An inclusive workplace with accommodations available.

• A transparent, collaborative, and respectful company culture.