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Checkout.com is a global fintech platform powering over 10 billion transactions yearly for companies like eBay, Spotify, Klarna, Uber, and Sony. The Payment Performance division uses machine learning to improve payment acceptance, reduce costs, and protect against fraud.
You will lead a distributed Data Science organization combining Fraud and Acceptance teams under one strategy and operating model. This is a player-coach role: you set direction and develop the organization while remaining technically engaged in design reviews, prototypes, and key analyses.
Key responsibilities include:
**Strategy & Impact**: Create a unified Data Science strategy across fraud and payment optimization. Decide investment priorities across models, data, experimentation, and shared capabilities. Identify competitive advantages through new scientific methods or data assets. Own the end-to-end model lifecycle from problem framing through production performance, ensuring teams optimize for business and merchant outcomes rather than research metrics alone.
**Technical Leadership**: Raise standards for experimentation, explainability, monitoring, and drift detection. Work with Engineering to make productionization a shared responsibility—Data Science owns model performance; Engineering owns platform reliability. Act as a technical sounding board for senior scientists on modeling, experimentation, and ML-system design. Challenge teams to choose practical methods that fit business problems.
**Cross-functional Collaboration**: Jointly prioritize roadmaps with Product Managers. Ensure Product owns merchant problems and commercial trade-offs while Data Science owns scientific direction and model quality. Translate complex technical choices into clear product and investment decisions. Partner with Engineering leaders on architecture for low-latency, resilient ML systems. Spend time with merchants to uncover better problems and demonstrate value.
**Organization & Development**: Lead and coach Data Scientists across Fraud and Acceptance, initially managing much of the team directly. Create clear ownership, goals, and development paths. Develop future domain leads and technical managers. Hire exceptional scientists and build a culture combining scientific rigor, pace, curiosity, and accountability. Create an environment where senior scientists can challenge assumptions and connect their work to measurable outcomes.
**Staying Current**: Keep the organization current on applied AI and machine learning developments, translating relevant advances into production opportunities.
Success in 12–18 months means uniting both teams behind one strategy, improving production impact across fraud and cost optimization, establishing clear decision rights with Product and Engineering, raising development standards, and developing future technical and people leaders.
You will lead colleagues across Europe and Tel Aviv, with occasional travel for team and product planning. You report to the Senior Director, Product and work closely with leaders across Product, Engineering, Analytics, and Commercial. The role follows a hybrid model with three days per week in the London office.
**Requirements:**
Essential:
- Strong record of leading Data Scientists who build real-time or high-scale machine-learning products
- Deep technical judgment across applied machine learning, experimentation, model evaluation, and production ML systems
- Experience taking models from notebooks and offline metrics into reliable products with measurable business impact
- Ability to coach scientists at different levels and challenge senior technical contributors with credibility
- Proven success leading distributed teams through change, ambiguity, and growth
- Strong product judgment connecting scientific work to customer problems, commercial trade-offs, and company strategy
- Confidence to influence senior leaders, challenge priorities, and explain complex technical ideas simply
- Practical, curious mindset with bias toward shipping, learning, and improving
- Company values scale and impact of systems and teams led over fixed years in a particular title
Valuable but not required:
- Experience in payment fraud, authorization optimization, routing, recommendations, or real-time decisioning domains
- Knowledge of highly imbalanced classification, anomaly detection, graph methods, reinforcement learning, or adaptive optimization
- Experience building low-latency models in regulated or financially sensitive environments
- Familiarity with causal inference, online experimentation, and measuring incremental model impact
- Experience developing first-line managers or technical leads within growing Data Science organizations
