About PayPay Card

PayPay Card Corporation was established in 2021 to provide users a FinTech service that is more accessible and convenient compared to previous credit cards and credit services, by integrating with the PayPay payment platform, which has surpassed 70 million users since its launch (as of July 2025).

We are looking for people who are passionate about refining our products at an overwhelming speed that other companies cannot match, as well as professionals who are interested in promoting the spread of cashless payments in Japan and the use of these payments as a financial life platform. Let us work together to create new value for users.

※ Please note that you cannot apply or be selected in parallel with PayPay Corporation, PayPay Card Corporation and PayPay Securities Corporation.

Job Description

Data analysis is an indispensable and crucial position for our growth. We strive to enhance our services by leveraging the insights gained from the vast amounts of data accumulated daily. We are moving beyond standard analytics and are heavily investing in statistical modeling, machine learning, and generative AI to predict user behavior, automate our processes, and provide a convenient and secure payment experience for our users.
データ分析は私たちの成長になくてはならない重要なポジションです。私たちはユーザーにとってより便利で安全な決済体験を提供するため、日々蓄積される膨大なデータから得た分析結果を駆使し、さらに統計モデリング、機械学習、生成AIの活用に大きく投資することで、サービスの向上を目指しています。

What Makes This Role Unique

Based on our internal direction, we're not just looking for an analyst to run queries. We're looking for a builder who wants to:

Work with Massive, Complex Data: You won't be data-starved. You will have access to one of the richest FinTech datasets in Japan, spanning the entire PayPay ecosystem.
Build the Foundation, Not Just Maintain It: We are in a high-growth phase of transforming our processes. You will help build the data-driven culture you’ve always wanted to work in.
Solve High-Stakes Product Challenges: Your models and analyses will directly influence our most critical projects.

Main Responsibilities

Partner with Product Managers and stakeholders to define key metrics, shape product strategy, and identify new opportunities for growth using advanced data analysis.
Develop, deploy, and maintain statistical models and ML algorithms (e.g., Python/R) for clustering, segmentation, and predictive analytics to uncover deep insights into user behavior.
Measure the causal impact of launched projects, distinguishing correlation from causation to understand what truly drives user behavior.
Help product managers and business leaders formulate and test hypotheses through data analysis and experimentation (e.g., A/B testing).
Proactively identify insights and opportunities from data, translating complex modeling results into a clear business narrative and actionable recommendations.
Partner with engineering and product teams on leveraging Generative AI to solve core user problems, including analyzing unstructured data from customer support.
Build views, tables, and data models on Bigquery using SQL to organize and transform datasets for analysis and feature engineering.
Maintain and automate key dashboards (Looker Studio) for business metrics and communicate insights to stakeholders on a regular basis.
プロダクトマネージャーやステークホルダーのパートナーとして、主要指標の定義、プロダクト戦略の形成、新たな成長機会の特定に高度なデータ分析を用いて貢献する。
PythonやRを用い、クラスタリング、セグメンテーション、予測分析などの統計モデルや機械学習アルゴリズムを開発・デプロイ・維持し、ユーザー行動の深い洞察を発見する。
リリースされたプロジェクトの因果的な影響を測定し、分析結果から相関関係と因果関係を切り分け、何がユーザー行動の真の要因であるかを理解する。
データ分析や実験（A/Bテストなど）を通じて、プロダクトマネージャーやビジネスリーダーが仮説を構築し、検証するプロセスをサポートする。
データから自主的（プロアクティブ）に洞察や機会を特定し、複雑なモデリング結果を明確なビジネスストーリーと実行可能な推奨事項に変換する。
カスタマーサポートの非構造化データ分析など、生成AIを活用したプロダクトチームとの連携。
SQLを使用しBigQuery上にビューやテーブル、データモデルを作成し、分析とフィーチャーエンジニアリング用のデータセットを整理・加工する。
主要なビジネス指標のダッシュボード（Looker Studio）を保持・自動化し、定期的なステークホルダーへ洞察を報告する。

Required Qualifications

Strong proficiency in Python or R and associated data science libraries (e.g., Pandas, scikit-learn, statsmodels, Tidyverse).
Proven experience in applying statistical modeling and ML techniques (e.g., logistic regression, clustering, classification, predictive analytics) to real-world business problems.
At least 3 years of analytical experience with advanced SQL.
Experience in designing, executing, and analyzing A/B tests or other controlled experiments.
Deep understanding of key statistical concepts (e.g., statistical significance, confidence intervals).
Ability to translate complex data findings into a clear business narrative and actionable recommendations for stakeholders.
English language in message communication (>= Communication Level English)
Business level of Japanese language in communication (>= JLPT N1)
PythonまたはRと、関連するデータサイエンスライブラリ（例：Pandas, scikit-learn, statsmodels, Tidyverse）の高い習熟度。
実世界のビジネス課題に対し、統計モデリングや機械学習の手法（例：ロジスティック回帰、クラスタリング、分類、予測分析）を適用した実務経験。
3年以上の高度なSQL分析経験
A/Bテストやその他の管理された実験の設計、実行、分析の経験
主要な統計概念（例：統計的有意性、信頼区間）への深い理解
複雑な分析結果を、ステークホルダー向けの明確なビジネスストーリーと実行可能な推奨事項に変換する能力
英語コミュニケーションレベル以上 (メッセージのやり取りがメイン)
ビジネス以上の日本語力 (JLPT N1)

Preferred Qualifications

Experience with Natural Language Processing (NLP), LLMs, or analyzing unstructured text data (e.g., customer support logs).
Deep curiosity about the "why" behind data and a strong interest in the FinTech/payments industry.
Track record of working in a very fast paced environment or a startup
Worked with a Product Manager to propose business/product recommendations
Experience in building data warehouses/data marts
Ownership, willingness to work hard, and fearlessness to move forward
Experience promoting a data-driven culture within a company
自然言語処理（NLP）、LLM、または非構造化テキストデータ（例：カスタマーサポートログ）の分析経験。
データの背景にある「なぜ」に対する強い好奇心と、フィンテック/決済業界のビジネスそのものへの強い関心
スピーディな環境、またはスタートアップでのでの就業経験
プロダクトマネージャーと協力してビジネス/プロダクトの提案を行った経験
データウェアハウスやデータマートの構築経験
自主性があり、意欲的かつ、業務を推進していく姿勢をお持ちの方
社内でデータドリブンなカルチャーを推進した経験

Working Conditions

Employment Status

Full Time

Office Location

Hybrid Workstyle (flexible working style including Remote and office)
※There are no fixed rules regarding office attendance in Technology group; it depends on each individual's discretion.

Work Hours

Full Flex Time (No Core Time)
In principle, 9:00am ~ 5:45pm (actual working hours: 7h45m + 1h break)

Holidays

Every Sat/Sun/National holidays (In Japan)/New Year's break/Company-designated Special days

Paid leave

Annual leave (up to 14 days in the first year, granted proportionally according to the month of employment. Can be used from the date of hire)
Personal leave (5 days each year, granted proportionally according to the month of employment)
＊PayPay Group's own special paid leave system, which can be used to attend to illnesses, injuries, hospital visits, etc., of the employee, family members, pets, etc.

Salary

Annual salary paid in 12 installments (monthly)
Reviewed once a year
Special Incentive once a year *Based on company performance and individual contribution and evaluation
Overtime allowance, Late overtime allowance, Commuting and transportation expenses

Benefits

Social Insurance (health insurance, employee pension, employment insurance and compensation insurance)
401K

Other Information

PayPay Inside-Out (Corporate Blog)
- JP
- ENG
Recruiting FACTBOOK for PayPay Card
- JP

Data Analyst / データアナリスト