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

Pay
Pay not listed
Work setting
Hybrid — work setting source
Listed location: Hybrid
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Employment
Full-time — employment source
Employment Status Full Time Office Location
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Before you apply

Work authorization
Sponsors visas

What you’ll work on

Full posting
  • Own ML models from problem framing through production and ongoing maintenance

  • Lead architectural decisions for data science systems

  • Analyze and visualize user and merchant data to inform product strategy

  • Build production systems with data engineers, product managers and stakeholders

From the employer’s posting
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

What you’ll bring

All qualifications

Core experience

  • Experience in Python and SQL (any variant)

Preferred experience

  • 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
Qualification wording
Experience in Python and SQL (any variant)
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
Education & alternatives
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

Benefits in the posting

Full benefits wording
  • Social Insurance (health insurance, employee pension, employment insurance and compensation insurance)
  • 401K
  • Translation/Interpretation support
  • VISA sponsor + Relocation support
  • Other Information:
  • PayPay Product Blog /JP
  • PayPay Product Blog /ENG

From the employer’s posting.

About PayPay

PayPay is a FinTech company that has grown to over 75M (as of August 2026) users since its launch in 2018.

In the employer’s words · Read in context

Apply at PayPay

Posting record

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Open 5 monthsWe first saw this posting 160 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
May 28, 2026160 days ago
Recorded sightings
82
Last seen by us
Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 160

Up longer than most. Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

PayPay is hiring

4 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • AWS
  • BigQuery
  • Databricks
  • Hadoop
  • Kafka
  • Redshift
  • S3
  • SageMaker
  • Snowflake
  • Spark
Where the posting names them
- 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
- 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 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 with AWS services such as Glue, SageMaker, Athena, and S3 - Experience with Databricks or Snowflake - Experience designing and conducting A/B and hypothesis tests

Your next step

Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.

Applying through Greenhouse

  • No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
  • Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.

Already applied? Track this application

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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.

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 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.

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

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.

Salary

  • Annual salary paid in 12 installments (monthly)
  • Based on skills, experience, and abilities
  • Reviewed once a year
  • 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
  • Translation/Interpretation support
  • VISA sponsor + Relocation support

Other Information:

Source details

View original posting ↗

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Pay

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Location & working pattern

Hybrid

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 authorization
- Translation/Interpretation support - VISA sponsor + Relocation support Other Information:

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