Head of Data Science

What do we do?

Paddle offers digital product companies a completely different approach to their payment infrastructure. Instead of assembling and maintaining a complex stack of payments-related apps and services, we’re a Merchant of Record for our customers. That means we take away 100% of the pain of payment fragmentation. It’s faster, safer, cheaper, and, above all, way better.

We’re backed by investors including KKR, FTV Capital, Kindred, Notion, and 83North and serve over 6000 software sellers in 245 territories globally.

The role:

We are looking for a Head of Data Science to build Paddle's data science capability from the ground up. Our Merchant of Record model gives us a data position no PSP or billing provider has: subscription and pricing context alongside granular payment-outcome data, across thousands of software businesses. This role exists to turn that into economic value by putting machine learning and agentic systems into production.

The mandate is deliberately narrow and deliberately ambitious: data science at Paddle owns automated decisioning inside the product — traditional machine learning and agentic systems alike — not decision-support analytics.

We have a long list of candidate opportunities than we can fund, spanning payment performance and revenue recovery, sales and marketing operations, growth and monetisation intelligence, and risk, trust and compliance. We have a working hypothesis about which of these pays back first, and we'll share it — but part of the job in your first quarter is to pressure-test it, size the alternatives yourself, and tell us where to start.

This is a founding role, and for the first few quarters it is a building role more than a managing one. You'll be the only person in the function: doing the analysis, engineering the features, training and evaluating the models or agents, taking the first system live with our engineering teams — and then operating it, answerable for its uptime, its drift and its numbers. Once the first use cases are proving out, you'll hire and lead a hub-and-spoke team of data scientists and machine learning engineers embedded across our highest-value business areas. You'll report into the VP of Data and work in close partnership with Product, Payments, Engineering, Risk and Finance.

What you'll do:

• Prioritise which opportunities have the biggest impact.. 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; size the leading candidates properly; and make the call on sequencing with the relevant Product, Risk, RevOps and Finance stakeholders. This gets refreshed quarterly.
• Personally deliver the first system end to end: the analysis and back-test, the features, the model, policy or agent, the deployment, and the shadow and A/B tests that prove it works. Not a spec handed to someone else to build.
• Operate what you deploy. Own monitoring, retraining, drift response, incident handling and rollback for live decisioning, alongside the engineering teams whose services call it — and set the expectation that the function runs its systems rather than shipping them.
• Work across both traditional ML and agentic systems, and be honest about which a problem actually needs: a propensity or uplift model, a policy of rules, or an agent with tools, evals against golden answer sets and trace-level observability. Several of our strongest candidate use cases point each way.
• Build and lead the team — hire senior data scientists embedded in value areas and machine learning engineers in the hub, and set the professional standards, shared methods and reusable components the function runs on.
• Establish the production stack alongside Data Platform and Engineering: reproducible training data, code and artefacts; a model registry; inference services with real latency, availability and rollback requirements; historically accurate features where decisions need backdated reconstruction; eval harnesses and trace observability for agentic workflows; and monitoring for data quality, drift, model performance and economic outcomes. Start ad hoc where that's sufficient and platformise once the first use cases have shown what's actually needed.
• Own value capture end to end. Shadow-test and A/B test every deployment against the incumbent strategy, translate metric movement into a financial number on a methodology co-signed by Finance, and publish a quarterly report on realised value.
• Set the governance model for automated decisioning — proportionate risk assessment, clear ownership, latency and availability requirements, human escalation and rollback — working with Legal, Privacy, Compliance and Risk on GDPR, EU AI Act and payments obligations, and producing the evidence early enough to shape the design.
• Define the boundaries and the working relationship with Product Science, Analytics …