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Checkout.com is a global fintech platform powering over 10 billion transactions yearly for more than one billion shoppers. The company operates 20 offices across six continents with London as headquarters, serving major clients including eBay, Spotify, Klarna, Uber, and Sony.
As a Product Data Scientist, you will join a cross-functional team alongside product managers, designers, and software and analytics engineers. You'll focus on the Platforms and SMB domain, using data and analytical expertise to influence the strategy of Activation, Risk, and Configuration products. Your primary focus will be optimizing the merchant onboarding journey and scaling predictive automation.
Key responsibilities include:
- Building experiments and analysis frameworks to measure operational efficiency and ROI of new software releases and internal tooling updates for the Platforms & SMB domain
- Collaborating with Data Analytics Engineers and Software Engineers to ensure proper data logging and modeling for high-integrity business insights
- Applying statistical modeling and exploratory data analysis to identify bottlenecks and drop-off points in the merchant onboarding journey, delivering insights to help Product Managers prioritize automation initiatives
- Designing and running proof-of-concept machine learning models and heuristic solutions to evaluate the feasibility of proposed onboarding automations before engineering handoff
- Defining how product success is measured and collaborating on data collection strategies
- Building analytical frameworks and running insights to identify product improvement opportunities
You'll be part of a highly visible Data Analytics function that critically impacts company success, with strong support from Senior Data Scientists, Analytics Engineers, and Data Product Managers to develop your technical and data science practice.
The role operates on a hybrid model with three days per week in the London office to support collaboration and connection.
REQUIREMENTS:
- Proven experience in similar data science or product analytics roles in a high-growth tech environment
- Excellent data interrogation skills with SQL and ability to comfortably write, read, and iterate through Python scripts for data science analyses
- Foundational understanding of data science concepts including statistics, clustering, and basic NLP workflows (production ML deployment experience not required, but understanding how to apply ML to data tasks is essential)
- Strong analytical mindset with demonstrated ability to convert operational problems into structured, data-informed solutions
- Clear and precise communication skills, able to explain data insights and analytical logic to non-technical stakeholders including Product Managers and Operations teams
