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Paddle is a Merchant of Record platform for digital product companies, offering payment infrastructure that eliminates payment fragmentation. Backed by KKR, FTV Capital, Kindred, Notion, and 83North, Paddle serves over 6,000 software sellers across 245 territories.
You will build Paddle's data science capability from the ground up, focusing on putting machine learning and agentic systems into production. The mandate is deliberately narrow and ambitious: data science owns automated decisioning inside the product—traditional ML and agentic systems—not decision-support analytics. Candidate opportunities span payment performance and revenue recovery, sales and marketing operations, growth and monetisation intelligence, and risk, trust and compliance.
In your first quarters, this is a building role more than a managing one. You will be the only person in the function: doing analysis, engineering features, training and evaluating models or agents, and taking the first system live with engineering teams. You'll operate what you deploy, owning monitoring, retraining, drift response, incident handling and rollback. Once use cases prove out, you'll hire and lead a hub-and-spoke team of data scientists and ML engineers embedded across high-value business areas. You'll report to the VP of Data and partner closely with Product, Payments, Engineering, Risk and Finance.
Key responsibilities include: prioritising opportunities with biggest impact and building a value-based use-case backlog; personally delivering the first system end-to-end (analysis, back-test, features, model, deployment, testing); operating what you deploy with full ownership of monitoring and incident response; working across both traditional ML and agentic systems; building and leading the team once initial systems prove out; establishing the production stack with Data Platform and Engineering; owning value capture end-to-end with shadow-testing and A/B testing; setting governance for automated decisioning in compliance with GDPR, EU AI Act and payments obligations; and delivering cross-functionally with product owners, domain experts and platform engineers.
You'll work in a digital-first environment with the option to work remotely, from stylish hubs, or hybrid. Paddle offers unlimited holidays, 4 months paid family leave regardless of gender, an annual learning fund, and regular training.
REQUIREMENTS:
- Proven experience leading data science or ML teams that own systems in production, with deployments that moved a commercial metric and kept running afterwards
- Hands-on technical skills now: writing SQL and Python, engineering features, evaluating models and agents, getting systems live
- Experience across both traditional ML (propensity/uplift models, feature pipelines, drift) and agentic systems (tool/context design, prompt iteration, evals, trace observability)
- Practised at running live systems: monitoring, retraining, incident response, rollback, on-call operations
- Pragmatic about method: comfortable with rules/heuristics where they work, reserving models/agents for genuine value
- Comfortable with undefined starting points and forming your own view backed by numbers
- Strong on measurement: experimentation, uplift modelling, back-testing, financial accountability
- Fluent in production ML/agent engineering: training pipelines, model registries, inference services, feature stores, drift detection, trace observability
- Able to hire, level and develop senior data scientists and ML engineers
- Credible with executives: can take use cases from business problem to value estimate to prioritisation
- Experience in payments, fintech, subscriptions, high-volume commercial operations or regulated transactional domains preferred
- Comfortable treating regulated ML as a design constraint: model risk assessment, DPIAs, auditability, traceability
