Senior Data Scientist - Payment Success
About The Position
Checkout.com is hiring a Senior Data Scientist for its Payment Success Management (PSM) team, which diagnoses and resolves payment performance problems for merchants. The role supports US-based merchants and works alongside account managers and technical account managers. The position focuses on finding the causes of payment performance gaps and helping merchants improve results through data analysis and experimentation. The role owns merchant performance engagements from kickoff through diagnosis, fixes, A/B testing, and reporting, typically over approximately three months. It involves analyzing large, complex datasets, developing and testing hypotheses, measuring uplift, and understanding how Checkout.com’s machine-learning optimization engine makes decisions. The Senior Data Scientist also identifies patterns in which the engine makes incorrect decisions and works with ML engineers to address them. Findings must be communicated clearly and persuasively to merchants and non-technical stakeholders. The role also uses automation and AI to improve team efficiency and shares learning across teams. Checkout.com describes a high-ownership, fast-moving environment focused on meaningful work, collaboration, and employee growth. Its hybrid working model involves three days per week in the office.
Requirements
- At least 2 years of experience using data to drive measurable results, such as revenue uplift, cost reduction, or efficiency gains, in a role involving ownership of metrics and decisions.
- Strong SQL and Python skills for analysis.
- Confidence checking queries and AI-generated code for accuracy.
- Natural curiosity and enjoyment of solving problems without ready-made answers.
- Ability to explain complex analysis clearly to non-technical audiences.
Nice To Haves
- Consulting background involving work between client teams and technical teams.
- Startup background involving end-to-end ownership and optimization.
Responsibilities
- Own merchant performance engagements from kickoff through diagnosis, fixes, A/B testing, and reporting results; engagements typically last about three months.
- Analyze large, complex datasets to identify the root causes of payment performance gaps.
- Turn findings into hypotheses, run A/B tests and other experiments, and measure uplift.
- Understand how the machine-learning optimization engine makes decisions, identify patterns in incorrect decisions, and work with the ML engineering team to address them.
- Present findings and results clearly and persuasively to merchants and non-technical stakeholders.
- Use automation and AI to improve the efficiency of the team’s day-to-day work.
- Share learnings across teams.
Benefits
- Hybrid working model with flexibility.
- Three days per week in the office to support collaboration and connection.
- Employee growth and meaningful work with real ownership.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
