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Paddle is a Merchant of Record platform serving over 6,000 software sellers globally, backed by KKR, FTV Capital, Kindred, Notion, and 83North. The company offers digital product companies a complete alternative to fragmented payment infrastructure, handling payments across 245 territories.
You will build Paddle's data science function from the ground up, focusing exclusively on automated decisioning systems—traditional machine learning and agentic systems deployed in production—rather than decision-support analytics. This is a founding role that combines hands-on technical work with team leadership.
In your first quarters, you will personally deliver end-to-end systems: conducting analysis and backtests, engineering features, training and evaluating models or agents, deploying with engineering teams, and operating live systems. You'll own monitoring, retraining, drift response, incident handling, and rollback. You'll work across payment performance and revenue recovery, sales and marketing operations, growth and monetization intelligence, and risk/trust/compliance use cases.
Key responsibilities include: prioritizing high-impact opportunities and building a value-based use-case backlog; personally delivering the first system end-to-end; operating deployed systems with accountability for uptime and performance; working across both traditional ML and agentic systems with clarity on which approach each problem needs; building and leading a hub-and-spoke team of data scientists and ML engineers embedded across business areas; establishing the production stack (training data, model registry, inference services, feature stores, eval harnesses, monitoring); owning value capture end-to-end with shadow testing, A/B testing, and financial quantification; setting governance for automated decisioning aligned with GDPR, EU AI Act, and payments obligations; defining boundaries with Product Science, Analytics Engineering, and other functions; and delivering cross-functionally embedded in delivery groups.
You'll report to the VP of Data and partner closely with Product, Payments, Engineering, Risk, and Finance teams.
REQUIREMENTS:
- Proven experience leading data science or ML teams that own production systems with deployments that moved commercial metrics and continued operating afterward (not POCs or dashboards)
- Hands-on technical skills now: SQL, Python, feature engineering, model and agent evaluation, system deployment
- Expertise across both traditional ML (propensity/uplift models, feature pipelines, drift detection) and agentic systems (tool/context design, prompt iteration, evals, trace observability)
- Demonstrated experience running live systems: monitoring, retraining, incident response, rollback, on-call operations
- Pragmatic approach to methodology: comfortable with rules/heuristics where they deliver value; reserves models/agents for justified use cases
- Comfort with undefined starting points and forming independent views backed by numbers
- Strong measurement and experimentation skills: backtesting, uplift modeling, financial rigor
- Fluency in production ML/agent engineering: training pipelines, model registries, inference services, feature stores, drift detection, trace observability; able to discuss latency, availability, observability, and rollback with senior engineers
- Ability to hire, level, and develop senior data scientists and ML engineers; set standards for a new function
- Executive credibility: translate business problems to value estimates, prioritize, and decline low-value work
- Experience in payments, fintech, subscriptions, high-volume commercial operations, or regulated transactional domains preferred
- Comfort treating regulated ML as a design constraint: model risk assessment, DPIAs, auditability, version traceability
