Head of Data Science
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Job Description
Head of Data Science building production ML and agentic systems for Paddle’s digital payments infrastructure. Leading automated decisioning, value measurement and the future data science team.
Responsibilities:
- Build Paddle's data science capability from the ground up
- Prioritise and size automated decisioning opportunities 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, including analysis, back-testing, feature engineering, model or agent development, deployment, shadow testing and A/B testing
- Operate deployed systems, owning monitoring, retraining, drift response, incident handling and rollback
- Determine whether each problem requires rules, traditional ML or agentic systems
- Hire and lead a hub-and-spoke team of data scientists and machine learning engineers
- Establish the production stack with Data Platform and Engineering, including training data, artefacts, model registry, inference services, historical features, evaluation harnesses, trace observability and monitoring
- Own value capture by measuring deployments against incumbent strategies, translating metric movement into financial value with Finance, and publishing quarterly reports
- Set governance for automated decisioning in partnership with Legal, Privacy, Compliance and Risk, including GDPR, EU AI Act and payments obligations
- Define boundaries with Product Science, Analytics Engineering, Data Platform and AI Enablement
- Collaborate with Product, Payments, Engineering, Risk and Finance and embed in delivery groups
- Report to the VP of Data
Requirements:
- Experience leading data science or ML teams that own systems in production
- Hands-on experience writing SQL and Python, engineering features, evaluating models and agents, and taking systems live
- Experience across traditional ML 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
- Experience operating live systems, including monitoring, retraining, incident response and rollback
- Experience with experimentation, uplift modelling and back-testing
- Fluency in production ML and agent engineering, including training pipelines, model registries, inference services, feature stores, drift detection and trace observability
- Ability to hire, level and develop senior data scientists and ML engineers
- Experience communicating with executives and commercial stakeholders
- Experience in payments, fintech, subscriptions, high-volume commercial operations or another regulated transactional domain
- Experience with model risk assessment, DPIAs, auditability and traceability of model and policy versions
- No specific educational credential required; Paddle states it does not care where candidates studied
- Must answer whether visa sponsorship will be required
Benefits:
- Unlimited holidays
- 4 months paid family leave regardless of gender
- Remote work, office hub work, or a combination of both
- Annual learning fund
- Regular internal and external training
- Personal development support
- Inclusive workplace and support for accommodations
- Transparent, collaborative and respectful culture
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