Head of Data Science

Job not on LinkedIn

🔥 0 minutes ago

🌐 United Kingdom, Portugal, +1 more countries – Remote

🇬🇧 United Kingdom – Remote 🇵🇹 Portugal – Remote 🇮🇪 Ireland – Remote

⏰ Full Time

🔴 Lead

📊 Data Scientist

🇬🇧 UK Skilled Worker Visa Sponsor

This company has sponsored UK Skilled Worker visas in the past.

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Paddle

WebsiteLinkedInAll Job Openings

201 - 500 employees

☁️ SaaS

💳 Fintech

🤝 B2B

💰 Debt Financing on 2022-05

SaaS • Fintech • B2B

Paddle is a comprehensive merchant of record and payment solution specifically designed for SaaS businesses. It provides a complete suite of products and services including billing, subscription management, pricing strategies, tax compliance, fraud protection, and more. Paddle helps software companies manage payments, streamline billing, and enhance customer retention with tools like ProfitWell Metrics and Price Intelligently. By handling sales taxes, fraud liability, and all aspects of the billing process, Paddle enables SaaS businesses to focus on growth without worrying about the complexities of global compliance.

📋 Description

• 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