- Страна
- Япония
Откликайтесь
на вакансии с ИИ

Senior Data Science Engineer / Senior Machine Learning Engineer - Data Insights
Отличная вакансия в одном из крупнейших финтех-единорогов Японии. Предлагает уникальную гибкость (Super Flex Time), поддержку при релокации и возможность работать в международной среде с передовым стеком технологий.
Сложность вакансии
Высокая сложность обусловлена требованиями к опыту (от 5 лет), необходимостью владения стеком Big Data (Spark, BigQuery) и опытом вывода моделей в продакшн (MLOps). Также ожидается высокий уровень самостоятельности в принятии архитектурных решений.
Анализ зарплаты
Зарплата в PayPay для Senior-позиций обычно находится на верхнем уровне японского рынка, часто превышая средние показатели для местных компаний за счет бонусов и гибких условий. Указанный диапазон соответствует уровню Tier-1 технологических компаний в Токио.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Data Science/ML Engineer position at PayPay. With over five years of experience in developing and deploying machine learning models, I have a proven track record of building end-to-end solutions that drive product growth and enhance user experience. My expertise in Python, SQL, and big data technologies like Spark and BigQuery aligns perfectly with your team's mission to power PayPay products through scientific insights and robust engineering.
In my previous roles, I have successfully led architectural decisions for complex data systems and collaborated closely with cross-functional teams to deliver scalable production models. I am particularly drawn to PayPay's "Unlimited" vision and the opportunity to work in such a diverse, fast-paced environment. I am confident that my background in statistical inference and optimization, combined with my passion for building high-impact fintech products, makes me an ideal candidate for the Data Insights department.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в paypay уже сейчас
Присоединяйтесь к команде PayPay и создавайте будущее финтеха в Японии, используя передовые технологии машинного обучения!
Описание вакансии
About PayPay
PayPay is a FinTech company that has grown to over 70M (as of July 2025) users since its launch in 2018. Our team is hugely diverse with members from over 50 different countries.
OUR VISION IS UNLIMITED_
We dare to believe that we do not need a clear vision to create a future beyond our imagination. PayPay will always stay true to our roots and realize a vision (future) that no one else can imagine by constantly taking risks and challenging ourselves. With this mindset, you will be presented with new and exciting opportunities on a daily basis and have the opportunity to grow and reach new dimensions that you could never have imagined. We are looking for people who can embrace this challenge, refresh the product at breakneck speed and promote PayPay with professionalism and passion.
※ Please note that you cannot apply or be selected in parallel with PayPay Corporation, PayPay Card Corporation and PayPay Securities Corporation.
Job Description
PayPay's growth is driving a rapid expansion of PayPay product teams, and the need for a robust data platform that drives cutting-edge data science and powers machine learning innovations is more critical than ever in order to support our growing business needs. We are looking for a Senior Data Science Engineer or Senior Machine Learning Engineer for the Data Insights department.
Team Missions
- The team's primary focus is building and deploying models that directly power PayPay products, with secondary responsibility for experimentation and data-driven insights.
- The team drives product improvements by engineering systems founded on a scientific understanding of user and merchant behavior.
- The scope of work spans engineering, product science, data science, machine learning, statistical inference, optimization, and BI analytics.
Responsibilities
- Own end-to-end design, implementation, evaluation, and maintenance of machine learning models for prediction, recommendation, anti-fraud, etc. from problem framing to production
- Lead architectural decisions for data science systems. Process, analyze, and visualize user and merchant data, providing data-driven insights that influence product strategy for technical and business divisions.
- Collaborate with data engineers, product managers, and stakeholders to build robust production systems
Required Qualifications
- Bachelors in a quantitative field such as Computer Science, Machine Learning, Mathematics, Statistics, Economics, Physics, or equivalent
- Verbal and written communication skills in English. English is the primary working language for the team; Japanese is beneficial for cross-functional collaboration.
- More than five years of work experience as a data scientist, machine learning engineer, or equivalent role
- Experience in Python and SQL (any variant)
Preferred Qualifications
- Masters or PhD in a quantitative field such as Computer Science, Machine Learning, Mathematics, Statistics, Economics, Physics, or equivalent
- More than seven years of experience as a data scientist, machine learning engineer, or equivalent role
- Experience with Big Data technologies like BigQuery, Spark, Hadoop, AWS Redshift, Kafka, or Kinesis streaming
- Experience with recommendation systems, deep learning, NLP, optimization, or anti-fraud systems
- Experience with AWS services such as Glue, SageMaker, Athena, and S3
- Experience with Databricks or Snowflake
- Experience designing and conducting A/B and hypothesis tests
- Experience building and maintaining microservices
- Verbal and written communication skills in Japanese
PayPay 5 senses
- Please refer PayPay 5 sensesto learn what we value at work.
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 Product group; it depends on each individual's discretion.
Work Hours
- Super Flex Time (No Core Time)
- In principle, 9:00am-5:45pm + 1h break (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'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)
- Based on skills, experience, and abilities
- Reviewed once a year
- Special Incentive once a year \*Based on company performance and individual contribution and evaluation
- Late overtime allowance
※Payroll payment can be changed to digital salary payment “PayPay Paycheck” for an amount set by you
Benefits
- Social Insurance (health insurance, employee pension, employment insurance and compensation insurance)
- 401K
- Language Learning support
- Translation/Interpretation support
- VISA sponsor + Relocation support
Other Information:
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Python
- SQL
- Machine Learning
- BigQuery
- Spark
- Hadoop
- AWS Redshift
- Kafka
- Amazon Kinesis
- Deep Learning
- NLP
- Amazon SageMaker
- Databricks
- Snowflake
- Microservices
Возможные вопросы на собеседовании
Проверка опыта работы с полным циклом разработки ML-моделей.
Расскажите о самом сложном ML-проекте, который вы вывели в продакшн: с какими проблемами архитектуры вы столкнулись и как их решили?
Оценка навыков работы с большими данными, что критично для 70 млн пользователей PayPay.
Как бы вы спроектировали систему рекомендаций в реальном времени, учитывая огромный объем транзакций PayPay? Какие инструменты (например, Kafka, Spark) вы бы использовали?
Проверка умения интерпретировать данные для бизнеса.
Как вы подходите к проведению A/B тестов и как интерпретируете результаты, если метрики продукта и технические метрики модели противоречат друг другу?
Оценка опыта в специфической для финтеха области.
Какие методы машинного обучения вы считаете наиболее эффективными для борьбы с фродом (anti-fraud) в платежных системах и почему?
Проверка соответствия культуре PayPay (Super Flex, Hybrid).
Как вы организуете свою работу и взаимодействие с распределенной командой в условиях полной свободы выбора графика (Super Flex Time)?
Похожие вакансии
MLOps Engineer (ML pipelines / Kubernetes / Airflow)
Senior Data инженер
Senior MLOps Engineer (Platform Development / LLMOps)
Data Engineer / SAP HANA Developer (Senior)
Senior ML Engineer
Senior MLOps
1000+ офферов получено
Устали искать работу? Мы найдём её за вас
Quick Offer улучшит ваше резюме, подберёт лучшие вакансии и откликнется за вас. Результат — в 3 раза больше приглашений на собеседования и никакой рутины!
- Страна
- Япония