We are seeking an experienced Machine Learning Engineer with a strong background in end-to-end ML systems, cloud platforms, data pipelines, and model monitoring. Lead the development of cutting-edge machine learning software systems for credit modeling and default risk prediction. Your work will not only involve deploying these systems to production but also ensuring they meet the diverse needs of our users and merchants through rigorous testing.
Imagine working with petabyte-scale datasets, developing high-throughput, low-latency systems that serve models in production, and touching the lives of over 65 million customers daily. Collaborate with some of the most innovative engineering teams to bring machine learning functionalities into our in-house production systems, and watch your contributions make a tangible difference.
Responsibilities
- Design and implement credit ML models and systems, ensuring robust and scalable solutions.
- Develop end-to-end ML pipelines for credit modeling, including data collection, preprocessing, model training, and deployment.
- Collaborate with data scientists and software engineers to integrate ML models into production environments, enhancing lending systems.
- Utilize cloud platforms (AWS preferred, GCP, Azure) to scale ML solutions, manage resources, and optimize costs.
Qualifications
- 3+ years of professional experience, particularly in developing and implementing credit ML models or systems within the banking or FinTech industry.
- Educational background in Computer Science, Engineering, Mathematics or related field.
- English language only is welcome! Japanese bilingual ideal.
- Good understanding of supervised and unsupervised learning, deep learning, reinforcement learning, and ensemble methods.
- Proficiency in Python, PostgreSQL, Java/Scala.
- Strong knowledge of database management systems (e.g., MySQL, PostgreSQL), data wrangling, feature engineering, ETL processes, and SQL databases.
- Familiarity with tools such as Apache Spark.
- Experience with Docker, Kubernetes, and cloud platforms (AWS preferred). Knowledge of MLOps practices.
- Expertise in TensorFlow, PyTorch, Keras, scikit-learn, and XGBoost.
- Proficiency with Jupyter Notebooks, Git, Jenkins, Apache Spark, and Hadoop.
Expected personality
- Strong problem-solving and analytical skills.
- Excellent communication and collaboration abilities.
- Adaptability and a willingness to learn new technologies and techniques.
- Proactive mindset with the ability to think strategically about team and company needs.
- Ability to make suggestions and improvements independently.
- Logical communicator with the ability to coordinate smoothly with stakeholders.
Portrait
- Unparalleled speed: Discover for yourself the important things that need to be done and implement ways to reach the best results at the fastest speed possible for the organization
- Commitment: As a professional, commit to the growth and business goals of the organization and create impactful results by your ownership
- Logical thinking: Think logically and structurally to bring real communication
- Curiosity and questioning mind: Keep your curiosity about new things and your challenges along with a continuous questioning mind and enjoy such circumstances in a future-oriented manner
- Problem solving: Take a proper approach towards both explicit and potential business/organization challenges to lead solutions involving others
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