Head of Data Science

Paddle is seeking a Head of Data Science to build the company's data science capability from the ground up. The role owns automated decisioning inside the product, spanning traditional machine learning and agentic systems, and turns Paddle's Merchant of Record data into economic value by putting models and agents into production. It is a founding, building role initially, reporting into the VP of Data, and will later include hiring and leading a hub-and-spoke team of data scientists and machine learning engineers.

Responsibilities

Build a value-based use-case backlog across payment performance and revenue recovery, sales and marketing operations, growth and monetisation intelligence, and risk, trust and compliance, refreshed quarterly; Personally deliver the first system end to end, including analysis and back-test, features, model, policy or agent, deployment, and shadow and A/B tests; Operate deployed systems, owning monitoring, retraining, drift response, incident handling and rollback; Work across traditional ML and agentic systems and choose the appropriate approach for each problem; Build and lead the data science team, hiring senior data scientists and machine learning engineers and setting professional standards and shared methods; Establish the production stack alongside Data Platform and Engineering, including training data, model registry, inference services, feature reconstruction, eval harnesses, trace observability and monitoring; Own value capture end to end, shadow-testing and A/B testing each deployment and publishing a quarterly report on realised value; Set the governance model for automated decisioning, working with Legal, Privacy, Compliance and Risk on GDPR, EU AI Act and payments obligations; Define boundaries and working relationships with Product Science, Analytics Engineering, Data Platform and AI Enablement; Deliver cross-functionally by embedding in delivery groups with product owners, domain experts and platform engineers

Requirements

Experienced leading data science or ML teams that own systems in production, with deployments that moved a commercial metric and kept running afterwards; Hands-on now, writing SQL and Python, engineering features, evaluating models and agents; Experienced across both traditional ML and agentic systems, including propensity and uplift models, feature pipelines, drift, tool and context design, prompt and retrieval iteration, evals against golden answer sets and trace observability; Practised at running live systems, including monitoring, retraining, incident response, rollback and on-call; Pragmatic about method, arguing for rules or heuristics where they capture most of the value; Comfortable with an undefined starting point and able to form, back with numbers and defend a view to an executive audience; Strong on measurement, including experimentation, uplift modelling and back-testing, and willing to hold work to a financial number Finance will scrutinise; Fluent in the engineering side of production ML and agents (training pipelines, model registries, inference services, feature stores, drift detection, trace observability) and able to hold their own with senior engineers on latency, availability, observability and rollback; Able to hire, level and develop senior data scientists and ML engineers and set standards for a new function; Credible with executives and commercial stakeholders, able to take a use case from business problem to value estimate to prioritisation decision and say no to low-value work; Experienced in payments, fintech, subscriptions, high-volume commercial operations or another regulated transactional domain; Comfortable treating regulated ML as a design constraint, including model risk assessment, DPIAs, auditability and traceability of model and policy versions to production decision logic

Benefits

Attractive salaries; Stock options; Unlimited holidays; Enhanced parental leave; Retirement plans; Private healthcare; Well-being initiatives; Annual learning fund; Regular internal and external training; Digital-first flexibility within hub locations