Machine Learning Engineer, Credit Modeling

PayPay; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

A leading FinTech company is looking for a Machine Learning Engineer to develop advanced ML systems for credit modeling. This remote role involves working with large datasets and collaborating with innovative teams to enhance our lending systems. Ideal candidates will have a strong background in ML, cloud platforms, and a passion for problem-solving.

Benefits

Flexible working style including remote and office

Annual leave up to 14 days

Personal leave for health-related issues

Social Insurance coverage

Qualifications

• 3+ years of experience in credit ML models or systems.
• Good understanding of supervised/unsupervised learning.
• Experience with Docker, Kubernetes, and cloud platforms.

Responsibilities

• Design and implement credit ML models and systems.
• Develop end-to-end ML pipelines for credit modeling.
• Collaborate to integrate ML models into production.

Skills

Problem-solving

Analytical skills

Communication

Collaboration

Adaptability

Education

Bachelor's degree in Computer Science, Engineering, Mathematics

Tools

Python

PostgreSQL

Java/Scala

Docker

Kubernetes

AWS

TensorFlow

PyTorch

Apache Spark

Job description

Machine Learning Engineer, Credit Modeling

Remote

About PayPay

PayPay is a FinTech company with over 69 million users as of May 2025. Our diverse team comprises members from over 50 countries.

OUR VISION IS UNLIMITED_

We believe in creating a future beyond imagination by taking risks and challenging ourselves. Join us to grow, innovate, and promote PayPay with passion.

※ Note: You cannot apply or be selected in parallel with PayPay Corporation, PayPay Card Corporation, and PayPay Securities Corporation.

Job Description

We seek an experienced Machine Learning Engineer skilled in end-to-end ML systems, cloud platforms, data pipelines, and model monitoring. Lead development of ML systems for credit modeling and risk prediction, deploying them to production and ensuring they meet user needs through rigorous testing.

Work with large datasets, develop high-performance systems, and collaborate with engineering teams to integrate ML functionalities, impacting millions of customers daily.

Responsibilities

• Design and implement scalable credit ML models and systems.
• Develop complete ML pipelines: data collection, preprocessing, training, deployment.
• Collaborate with data scientists and engineers to integrate models into production.
• Utilize cloud platforms (AWS, GCP, Azure) to scale and optimize ML solutions.

Qualifications

• 3+ years of experience in credit ML models or systems, preferably in banking or FinTech.
• Degree in Computer Science, Engineering, Mathematics, or related fields.
• English proficiency; Japanese bilingual preferred.
• Knowledge of supervised, unsupervised, deep, reinforcement learning, and ensemble methods.
• Proficiency in Python, PostgreSQL, Java/Scala.
• Experience with database management, ETL, data wrangling, SQL.
• Familiarity with Apache Spark, Docker, Kubernetes, cloud platforms, MLOps, TensorFlow, PyTorch, Keras, scikit-learn, XGBoost.

Expected Personality

• Strong problem-solving and analytical skills.
• Excellent communication and teamwork.
• Adaptability and eagerness to learn new tech.
• Proactive, strategic thinking, ownership mindset.
• Logical, structured communicator.
• Speed: Ability to identify priorities and execute swiftly.
• Commitment: Focus on growth and impactful results.
• Curiosity: Continuous learning and questioning.
• Problem-solving: Lead solutions collaboratively.

Workstyle: Hybrid (Remote and Office flexibility). Office attendance is at individual discretion.

Working hours: 9:00 am - 5:45 pm (7h45m + 1h break).

Holidays

• Saturdays, Sundays, Japanese national holidays, New Year’s, and company-designated days.

Paid Leave

• Annual leave (up to 14 days, proportional to hire date).
• Personal leave (5 days/year, proportional to hire date).
• Salary paid monthly, reviewed annually.
• Performance-based incentives and allowances.

Additional details on benefits and application procedures are provided in the original description.