Company Description
We’re Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.
We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.
Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.
If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.
With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.
The opportunity
Checkout.com helps businesses thrive in the digital economy by making payments work better. Within Payment Performance, Data Science is central to how we improve acceptance, reduce payment costs and protect merchants and Checkout from fraud.
We are looking for a Data Science leader to bring our Fraud and Acceptance Data Science teams together behind one strategy and way of working. The teams already exist and are building important products. Your job is to help them work as one organization, raise the bar for how models are developed and operated, and turn more of their work into measurable production impact.
You will lead a distributed group of Data Scientists working on real-time decisioning at global payments scale. The problems range from fraud detection and adaptive risk strategies to authorization optimization, routing and recovery. The common thread is using machine learning to make better decisions on every payment.
This is a player-coach role. You will set direction and develop the organization, but remain close enough to the work to challenge technical choices, lead design reviews and contribute directly when the problem calls for it.
What you’ll do
Set the Data Science strategy
Create one Data Science strategy across fraud and payment optimization, connected to the wider Payment Performance vision.
Decide where we should invest across models, data, experimentation and shared capabilities to create the greatest business impact.
Build common technical standards and ways of working without forcing different problem domains into the same approach.
Identify where new scientific methods or data assets could create a lasting competitive advantage.
Turn models into production impact
Own the end-to-end model lifecycle, from problem framing and experimentation through evaluation, launch and production performance.
Improve measurable outcomes across fraud, acceptance and payment cost, with models that operate safely and reliably at scale.
Ensure teams optimize for business and merchant outcomes rather than research output or offline model metrics alone.
Raise the standard for experimentation, explainability, monitoring, drift detection and model iteration.
Work with Engineering to make productionization a shared responsibility: Data Science owns model performance, while Engineering owns platform reliability and deployment infrastructure.
Partner with Product and Engineering
Jointly prioritize the roadmap with the Fraud and Intelligent Acceptance Group Product Managers.
Ensure Product owns merchant problems, desired outcomes and commercial trade-offs, while Data Science owns scientific direction, model quality and technical standards.
Translate complex scientific choices into clear product, customer and investment decisions.
Partner with Engineering leaders on the architecture and capabilities needed for low-latency, resilient ML systems.
Spend time with merchants when direct technical engagement can uncover a better problem, build confidence or demonstrate value.
Lead and grow the organization
Lead, coach and develop Data Scientists across Fraud and Acceptance, initially managing much of the team directly.
Create clear ownership, goals and development paths across a distributed organization.
Develop future domain leads who can take on more day-to-day technical and people leadership as the teams grow.
Hire exceptional scientists and build a culture that combines scientific rigor, pace, curiosity and accountability.
Create an environment where senior scientists can challenge assumptions, do their best work and connect it to outcomes that matter.
Stay close to the science
Act as a technical sounding board for senior scientists across modelling, experimentation and ML-system design.
Lead or contribute to design reviews, prototypes and selected analyses where your involvement can materially improve the outcome.
Challenge teams to choose practical methods that fit the business problem, rather than defaulting to the most complex approach.
Keep the organization current on developments in applied AI and machine learning, and translate relevant advances into production opportunities.
What success looks like
In your first 12–18 months, you will have:
United the Fraud and Acceptance teams behind one Data Science strategy and operating model.
Improved the production impact of models across fraud, acceptance and cost optimization.
Established clear decision rights and an effective joint roadmap with Product and Engineering.
Raised standards for model development, productionization, monitoring and measurement.
Created stronger consistency across the teams while preserving the expertise each domain requires.
Developed future technical and people leaders who can support the organization as it grows.
What we’re looking for
Essential
A 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 beyond notebooks and offline metrics into reliable products with measurable business impact.
The 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: you can connect scientific work to customer problems, commercial trade-offs and company strategy.
The confidence to influence senior leaders, challenge priorities and explain complex technical ideas in simple language.
A practical, curious mindset and a bias toward shipping, learning and improving.
We care more about the scale and impact of the systems and teams you have led than a fixed number of years in a particular title.
Valuable, but not required
Experience in payment fraud, authorization optimization, routing, recommendations or another real-time decisioning domain.
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 a growing Data Science organization.
How we work
This role is based in London and follows Checkout.com’s hybrid working model.
You will lead colleagues across Europe and Tel Aviv, with occasional travel for team and product planning. You will report to the Senior Director, Product and work closely with leaders across Product, Engineering, Analytics and Commercial within Payment Performance.
Additional Information
Bring all of you to work
We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one.
Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands.
We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.
It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.
Life at Checkout.com
We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Curious about what it’s like to be part of our team? Visit our Careers Page to learn more about our culture, open roles, and what drives us.
For a closer look at daily life at Checkout.com, follow us on LinkedIn and Instagram