Payment Efficiency Analyst in Fintech

Our team is responsible for the quality of the payment experience and the success rate of payment processing. We investigate where users get lost in the payment process, which technical or product factors hinder successful payment, how payment success is affected by timing, errors, routing, and scenario specifics, and determine what changes will help increase conversion.

What tasks await you

Payment Funnel Analysis You will break down the user's journey at the payment stage and find out which steps are causing the main drop-offs. This could be related to errors, delays, specifics of the payment method, differences between services, platforms, or user segments. It is important to turn a large volume of transactional events into a clear picture and determine where the problem lies.

Analysis of Factors Affecting Payment Conversion You will analyze how response time, step sequence, and error frequency affect payment conversion and user behavior. It is necessary to understand where the effect is significant and where it is just noise or a local peculiarity of the data.

A/B Experiment Analysis The team regularly tests hypotheses related to changes in the payment process: timing, order of displaying payment methods, routing, fallback scenarios, and other improvements. You will design the analysis, evaluate the effect, check the robustness of the results, and help the team make decisions about launching or refining changes.

Growth Hypotheses You will not only look for problems but also propose changes that will improve the payment experience: speeding up specific stages, changing the logic for displaying payment methods, adjusting routes, improving fallback scenarios, reducing the number of errors. It is important to be able to prioritize hypotheses and assess their impact on conversion and GMV.

Learn more about analytics at Yandex — in the channel Yandex for Analytics

We expect you

• Write proficiently in SQL and work with large datasets
• Use Python for data analysis: pandas, NumPy, or similar tools
• Find patterns in data and explain the causes of observed changes
• Structure information and clearly convey conclusions to product and technical teams
• Independently formulate analytical hypotheses and test them with data
• Are willing to constantly learn and master new technologies and approaches

Will be a plus

• Have worked with payments, fintech, or financial analytics
• Have analyzed A/B experiment results
• Have worked with large transactional data
• Understand the principles of payment systems, acquiring, and processing