Head of Data Science

Paddle; United States; Remote; USD 180,000 - 300,000; Full time

Job summary

Paddle is seeking a Head of Data Science to build its data science capability from the ground up. This founding role will own automated decisioning inside the product, leveraging subscription and pricing context with granular payment-outcome data.

You will work closely with Product, Payments, Engineering, Risk and Finance to identify opportunities, design and deploy end-to-end data solutions, and lead a hub-and-spoke team as the function scales.

Qualifications

• Experience building data science capabilities from the ground up.
• Delivery of end-to-end ML systems including features, models, deployment.
• Ability to work with product, risk, and finance stakeholders.

Responsibilities

• Prioritise opportunities with highest impact and build a backlog.
• Deliver first system end-to-end including back-testing and A/B tests.
• Own monitoring, retraining, drift, and performance of deployed models.

Skills

Data science

Machine learning

Leadership

End-to-end delivery

Job description

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, dri