機械学習エンジニア（金融事業領域）

About the role

PayPay is actively seeking a skilled machine learning engineer to join our dedicated team within the financial services division. In this pivotal role, you will be responsible for developing and deploying innovative machine learning models and systems that support our financial products and services. Your work will directly influence the enhancement of user experiences and the overall growth of our fintech offerings. This position provides an excellent opportunity to work in a fast-paced, collaborative environment where your technical expertise and creative problem-solving skills will be highly valued. You will collaborate with cross-functional teams, including data scientists, engineers, and product managers, to deliver scalable and reliable machine learning solutions that align with business objectives and regulatory requirements.

Key facts

What you'll do

• Collaborate with engineers and data scientists to develop, implement, and optimize machine learning-based financial services and products, guiding projects from initial requirements gathering through to deployment and monitoring.
• Define and establish software and quality requirements specific to machine learning systems, ensuring they meet both technical standards and business goals.
• Foster a culture of efficiency and quality by conducting code reviews, writing tests, and implementing best practices in software development and deployment.
• Design, develop, and maintain machine learning models that support various financial applications, including fraud detection, credit scoring, and customer segmentation.
• Work on data collection, cleaning, and feature engineering processes to ensure high-quality data inputs for model training and evaluation.
• Monitor the performance of deployed models, identify issues, and implement improvements to maintain accuracy and reliability over time.
• Develop and manage scalable ML pipelines and workflows, leveraging MLOps tools and frameworks to streamline deployment, versioning, and automation processes.
• Stay current with the latest advancements in machine learning, data science, and fintech to incorporate innovative techniques and tools into existing systems.
• Collaborate with product managers and stakeholders to understand business needs and translate them into technical requirements and solutions.
• Participate in cross-team meetings to align project goals, share insights, and contribute to the overall technical strategy of the division.
• Contribute to documentation and knowledge sharing within the team to promote best practices and continuous learning.
• Support compliance with regulatory standards and ITAR restrictions, ensuring all models and systems adhere to necessary security and privacy protocols.

Requirements

• Proven experience in developing products using Python, including familiarity with package management, testing tools, and linters.
• Strong understanding of MLOps practices, including designing architecture for ML pipelines, workflow management, and deployment strategies.
• Hands-on experience working with cloud services for deploying and managing machine learning applications.
• Proficiency in Japanese for effective communication with team members and stakeholders.
• Solid problem-solving skills and the ability to work collaboratively within a multidisciplinary team environment.
• At least 3 years of experience in machine learning, data science, or related fields, with a focus on product development.
• Experience in designing, implementing, and maintaining scalable ML systems in a production environment.
• Knowledge of data management, feature engineering, and model evaluation techniques.
• Familiarity with software development best practices, including version control, testing, and documentation.
• Ability to prioritize tasks and manage multiple projects simultaneously in a fast-moving environment.

Nice to have

• Contributions to open-source machine learning libraries or projects, demonstrating community engagement and technical expertise.
• Experience in developing internal MLOps infrastructure, including automation, monitoring, and model lifecycle management.
• Leadership experience managing teams of 5 to 10 members, with a focus on mentoring and project coordination.
• Research experience in machine learning or data science, including the implementation of academic papers and novel algorithms.
• A degree in data science, computer science, or a related technical field.
• Business-level English proficiency, with a TOEIC score of 800 or above, to facilitate communication with international partners and documentation.

Skills & tools

• Python programming language
• Machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar
• Cloud services including AWS, GCP, or Azure for deploying and managing ML applications
• MLOps tools and frameworks for automation, CI/CD, and model versioning
• Data management and processing tools such as SQL, Spark, or similar
• Version control systems like Git for collaborative development

Practical notes

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Employment Type: Full-time employee

• Work Style: Hybrid, with in-office attendance as per organizational guidelines and project needs.
• Work Hours: Super flex system, meaning there are no fixed core hours; generally from 9:00 AM to 5:45 PM, including a 1-hour break, allowing flexibility for individual productivity.
• Holidays: Company-observed holidays, weekends, national holidays, year-end and New Year holidays, and other designated days off.
• Annual Paid Leave: Starting with 14 days in the first year, prorated based on the month of joining, with the possibility of accruing additional days over time.
• Personal Leave: Up to 5 days per year, with specific provisions for personal or family health needs, including special leave for family emergencies.

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Salary: Annual salary system, which includes some fixed overtime pay, determined based on experience, skills, and performance. Salary reviews are conducted annually to reflect contributions and market standards.

• Benefits: Comprehensive social insurance coverage, including health, pension, employment, and workers' compensation insurance. Additionally, the company offers a corporate defined contribution pension plan to support long-term financial security.
• Compliance: All systems and models must adhere to ITAR restrictions and other regulatory standards, ensuring security and privacy are maintained at all times.

Join us at PayPay and be part of a forward-thinking team that is transforming the financial landscape through innovative machine learning solutions. We look forward to your application and the opportunity to work together to shape the future of fintech.