Data Scientist

Who We Are

PT Kredit Biro Indonesia Jaya (Credit Bureau Indonesia, “CBI”) as one of the private credit bureaus in Indonesia has been established since 2014 and obtained an operational license from Otoritas Jasa Keuangan (OJK) as Lembaga Pengelola Informasi Perkreditan (LPIP) in 2015.

In November 2021, after obtaining approval from OJK, CBI received additional capital from shareholders and appointed a new management team, which aims to provide new and leading-edge services and products. This new identity will also strengthen CBI's position as the "best-in-class, modern and innovative" credit bureau in Indonesia.

CBI is a trailblazer in the credit bureau industry, dedicated to fostering a culture of excellence and innovation. We believe in the power of a talented and diverse team to bring cutting-edge solutions to our clients and contribute to the overall success of the financial landscape in Indonesia.

Why Join Us

• Innovative Environment: Continuously investing in cutting-edge technologies, including the powerful realm of artificial intelligence, your role becomes a pivotal part of our journey into the dynamic and evolving landscape of financial services. Being part of our innovative environment is your gateway to an exciting and forward-thinking career
• Impactful Work: We offer more than just a job; we provide an opportunity to play a crucial role in shaping the financial future of individuals and businesses across the nation. As a licensed credit bureau, our commitment to accuracy, reliability, and timeliness sets the stage for impactful work
• Professional Development: We are committed to the growth of our employees. Join a team that encourages continuous learning and offers opportunities for career advancement.
• Collaborative Culture: Work in a collaborative and inclusive environment where your ideas are valued, and teamwork is key to achieving our goals.
  
  

Role Overview

A Data Scientist builds and optimizes data-driven solutions by developing machine learning models and maintaining end-to-end data science platforms, including feature engineering, model training, deployment, and monitoring. The role also involves improving technical frameworks, conducting data analysis (especially for credit scoring), and researching methodologies to enhance model performance.

Job Responsibilities

• Develop and maintain the fundamental platforms for the data science team, including feature engineering, model training, and model monitoring.
• Upgrade and refactor the basic technical frameworks within the team, covering model serving, code management, and other related infrastructure.
• Assist other data scientists in resolving technical issues related to model deployment and decommissioning.
• Research model methodology and data mining techniques to improve model performance
• Conduct data analysis and credit modeling tasks, ensuring the performance of modeling results.
  
  

Job Requirements

• Bachelor’s degree in Computer Science, Data Science, Statistics, or related fields.
• Minimum 3 years of experience in Machine Learning Engineering, MLOps, or related areas.
• Strong Python skills for automation, pipelines, and tooling; familiar with Linux and database design.
• Experience in ML platform development, including feature engineering, model training, evaluation, deployment, and monitoring.
• Hands-on experience with REST APIs, Docker, Kubernetes, workflow orchestration tools (Airflow, Prefect), and Git.
• Familiar with MLOps practices such as model versioning, lineage, reproducibility, and observability.
• Solid understanding of ML/DL algorithms (logistic regression, decision trees, random forests, neural networks).
• Experience in financial credit scoring models is mandatory.
• Comfortable working in regulated or data-sensitive environments.
• Good English communication skills and able to collaborate across teams.
• Experience in AI Agent implementation is a plus.
  
  

Nice to Have

• Experience with feature stores, CI/CD, Terraform, or Helm.
• Familiarity with risk models, financial data products, or model risk frameworks (e.g. SR 11-7).
• Background in statistical modeling or ML experimentation.