Own end-to-end design, implementation, evaluation, and maintenance of ML 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 to provide data-driven insights that influence product strategy.
Collaborate with data engineers, product managers, and stakeholders to build robust production systems.
Contribute to experimentation design and data-driven insights to drive product improvements.
Technical Stack
Required Skills
Bachelor's degree in a quantitative field (e.g., CS, ML, Math, Statistics, Economics, Physics)
5+ years as a data scientist, ML engineer, or equivalent
Proficiency in Python and SQL
English communication (verbal and written); Japanese beneficial for cross-functional collaboration
Preferred Skills
Master's or PhD in a quantitative field
7+ years of experience
Big Data: BigQuery, Spark, Hadoop, AWS Redshift, Kafka, or Kinesis
ML/DS domains: recommender systems, deep learning, NLP, optimization, anti-fraud
AWS: Glue, SageMaker, Athena, S3
Databricks or Snowflake
Designing/conducting A/B tests and hypothesis testing
Building and maintaining microservices
Japanese language proficiency
Career Growth Perspective
Opportunity to own the production ML lifecycle end to end, directly impacting PayPay products and data-driven decision-making.
Exposure to architecture leadership across DS systems, experimentation design, and cross-functional collaboration (data engineering, product, business).
Experience with large-scale data platforms and cloud/modern data tooling (BigQuery, Spark/Hadoop, Redshift, Kafka/Kinesis, AWS services, Databricks/Snowflake) in a FinTech context, enabling growth in technical breadth and leadership.