機械学習エンジニア条件を変える閉じる
Senior Data Science Engineer / Senior Machine Learning Engineer
給与
記載なし※ 募集要項に金額の記載がありません
- 勤務地
- 記載なし
- 雇用形態
- 正社員
- 働き方
- ハイブリッド勤務
- 年間休日
- 記載なし
在宅勤務あり
要約求人票をもとにAIがまとめたものです
The team builds and deploys machine learning models that power PayPay products, and is also responsible for experimentation and data-driven insights. The role owns the end-to-end design, implementation, evaluation, and maintenance of machine learning models, and leads architectural decisions for data science systems. It also processes, analyzes, and visualizes user and merchant data to provide insights that influence product strategy.
応募資格
必須
- 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)
歓迎
- 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
募集要項最終確認 10/2
- 職種
- 機械学習エンジニア
- 雇用形態
- Full Time
- 役職
- Senior Data Science EngineerSenior Machine Learning Engineer
- 給与
- 記載なし
- 給与の詳細
- Annual salary paid in 12 installments (monthly)Based on skills, experience, and abilitiesReviewed once a yearLate overtime allowance※Payroll payment can be changed to digital salary payment “PayPay Paycheck” for an amount set by you
- 手当
- Late overtime allowance
- 勤務地
- 記載なし
- 働き方
- Hybrid Workstyle※ You will be expected to work in the office, in alignment with organizational guidelines and team objectives.
- 勤務時間
- Super Flex Time (No Core Time)In principle, 9:00am-5:45pm + 1h break (actual working hours: 7h45m + 1h break)
- 休日・休暇
- Every Sat/SunNational holidays (In Japan)New Year's breakCompany-designated Special daysAnnual 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.
- 福利厚生
- * 401K* Translation/Interpretation support* VISA sponsor + Relocation support
- 社会保険
- * Social Insurance (health insurance, employee pension, employment insurance and compensation insurance)
- 経験年数
- 5年以上
- 学歴
- 大学卒業以上
- 掲載日
- 2026/07/02 21:29（92日前）
- 最終確認
- 2026/10/02 04:48（今日）
仕事内容
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
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