Senior Data Science Engineer / Senior Machine Learning Engineer (Applied Insights)

at Paypay

Hybrid, hybrid, senior, full time, 5+ years

posted 27 Jun 2023

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BigQuery
AWS Redshift
Hadoop
Kafka
AWS Athena
AWS Glue
Microservices
Deep Learning
SQL
NLP
Kinesis
AWS SageMaker
Snowflake
AWS S3
Spark
Databricks
Python

About the role

Posted: 2023-06-27

About PayPay

PayPay is a FinTech company that has grown to over 75M (as of August 2026) 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.

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 Applied 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.

What you would do

- 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.

Read from job-boards.greenhouse.io, last seen 3d ago.

What they are looking for

- 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)

- 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 senses to learn what we value at work.

Working Conditions

Employment Status

- Full Time

Office Location

- Hybrid Workstyle ※ You will be expected to work in the office, in alignment with organizational guidelines and team objectives.

- LIFE in JAPAN FACTBOOK

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.

About the role

What you would do

What they are looking for

What comes with it