At least 5 years of experience in data analysis or a highly quantitative field.
Strong applied knowledge of statistical modeling, including regression, classification, clustering, and time-series forecasting, and sound judgment in selecting methods.
Practical experience building predictive models on real-world business datasets to support operational or strategic decisions.
Advanced proficiency in SQL.
Ability to independently manage ambiguity and scope open-ended business questions.
Experience translating complex technical analysis into persuasive narratives for non-technical operational stakeholders.
Strong written and verbal English communication.
Responsibilities
Act as the primary data partner for operations and business teams, define analytical approaches for complex and ambiguous questions, and translate them into clear, data-driven strategies.
Own analytical projects end-to-end, from initial scoping and data extraction through modeling, delivery, and stakeholder presentation.
Design and develop advanced analytical models, including classification, clustering, forecasting, and other predictive techniques, to improve operational efficiency and support business growth.
Translate complex statistical analysis and model outputs into compelling narratives and business cases that influence decision-making among non-technical leadership.
Develop and maintain self-service reporting dashboards and analytical tools that allow stakeholders to access and interpret data independently.
Improve data quality, consistency, and governance across the analytics layer, partnering with data engineering on instrumentation gaps and schema design.
Document analyses, methodologies, assumptions, and business logic to support transparency, reproducibility, and knowledge sharing.
Desired Qualifications
Programming skills in Python or R for complex data transformation, analysis, and modeling.
Experience with payments or fintech-related data systems.
Experience with cloud machine learning tools such as BigQuery ML, SageMaker, or similar MLOps environments.
Experience with data visualization tools such as Looker and Tableau, and self-service business intelligence environments.
Business-level spoken Japanese.
About KOMOJU
KOMOJU is the leading cross-border payment gateway for Japan. We power payments for companies like video game distribution platform Steam and the popular mobile app TikTok. Today we help thousands of merchants by providing them with the payment infrastructure they need through developer-friendly API’s to integrations on popular platforms like Shopify and Wix; we help our merchants grow in all markets they are expanding.
What to expect
We're a business-focused engineering ops team, meaning your work will directly drive how we grow and optimize our operations. You'll work alongside our existing Data team in a flat and inclusive culture. Our team is self-organizing, which means you'll have full ownership over your projects and the ability to drive your own analytical work. You'll play to your strengths, but also have the opportunity to invest in areas where you want to grow. At KOMOJU, you are the main driver behind your growth and your impact on the company.
International at our core
Around half of our team members come from outside Japan. English is the primary language used within our engineering team, and throughout the company, many people are bilingual.
Being an international company we know the importance of bilingualism. We offer all employees a choice between optional English and Japanese lessons to help create a culture of diversity and ensure smooth collaboration across teams.
About the position
We are seeking a Senior Data Analyst to help scale and optimize the business through advanced analytics and data-driven decision making. This role partners closely with operations and business teams to uncover insights, identify opportunities, and develop analytical solutions that drive measurable outcomes.
The ideal candidate possesses strong technical expertise and thrives in solving complex business problems in ambiguous environments. They are able to independently lead analytical initiatives end-to-end, from identifying and framing business challenges, defining the appropriate analytical approach, and aligning stakeholders on the proposed direction, through to delivering actionable insights, implementing solutions, and measuring impact.
Responsibilities
Act as the primary data partner for operations and business teams, defining the analytical approach for complex, ambiguous questions and translating them into clear, data-driven strategies.
Take full ownership of analytical projects end-to-end, from initial scoping and data extraction through modeling, delivery, and stakeholder presentation.
Design and develop advanced analytical models, including classification, clustering, forecasting, and other predictive techniques, to improve operational efficiency and support business growth.
Translate complex statistical analysis and model outputs into compelling narratives and business cases that influence decision-making among non-technical leadership.
Develop and maintain self-service reporting dashboards and analytical tools that allow stakeholders to access and interpret data independently.
Improve data quality, consistency, and governance across the analytics layer, partnering with data engineering on instrumentation gaps and schema design.
Document analyses, methodologies, assumptions, and business logic to ensure transparency, reproducibility, and knowledge sharing.
5+ years of experience in data analysis or a highly quantitative field.
Strong applied knowledge of statistical modeling (e.g., regression, classification, clustering, time-series forecasting) and the judgment to know when to use each.
Practical experience building predictive models on real-world business datasets to support operational or strategic decisions.
Advanced SQL proficiency.
Proven ability to independently manage ambiguity and scope open-ended business questions.
Demonstrated experience turning complex technical analysis into a persuasive narrative for non-technical operational stakeholders.
Strong written and verbal English communication.
Nice to Haves
Programming skills in Python or R for complex data transformation, analysis, and modeling.
Experience with payments or fintech-related data systems.
Experience with cloud ML tools (e.g., BigQuery ML, SageMaker, or similar MLOps environments).
Experience with data visualization tools (e.g., Looker, Tableau) and self-service BI environments.
Business-level spoken Japanese.
10 days regular vacation, additional 5 days summer, and year-end holidays.
Paid birthday holiday.
Budget for self-learning allowance, to ensure our employees’ skills remain current.
Access to the O’Reilly Learning Platform.
Language training for Japanese/ English
Twice a week office lunch.
Learning allowance.
By submitting your application, you agree that your personal data will be processed in accordance with our Privacy Policy solely for recruitment and evaluation purposes.