Location
In office (Indonesia)
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Mar 1, 2024. OCBC NISP scores D on the Alion truth index.
Overview
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OCBC NISP is an Indonesian bank offering savings, business banking and investment solutions to personal and corporate customers. It hires premier banking business developers, enterprise banking credit analysts, treasury and wealth operations officers and marketing technology interns. The bank is headquartered in Jakarta, Indonesia.
Data Scientist
Do you have a passion for uncovering insights from data and applying machine learning (ML) to solve problems in the dynamic world of finance?
We are seeking a highly motivated and independent Data Scientist to join our growing team!
In this role, you will:
- Play a key role in driving data-driven decision making across the financial organization.
- Develop, implement, and maintain ML models to solve complex financial challenges.
- Work collaboratively with stakeholders across various business units to understand their needs and translate them into actionable data solutions.
- Clean, analyze, and interpret large datasets to identify trends and patterns.
- Communicate complex findings in a clear and concise manner to both technical and non-technical audiences.
- Stay up-to-date on the latest advancements in ML and financial technology (FinTech).
Qualifications:
- A strong understanding of fundamental algorithms and data structures (e.g., sorting, searching, trees, graphs).
- Proficiency in applying basic machine learning concepts (e.g., regression, classification, clustering).
- Experience working with programming languages like Python and R.
- Solid foundation in statistics and probability.
- Excellent communication and collaboration skills.
- A passion for learning and staying informed about the latest trends in data science and finance.
- A high degree of autonomy and the ability to manage multiple tasks simultaneously.
Bonus points if you have:
- Experience with financial data and regulations.
- Familiarity with cloud platforms (e.g., AWS, Azure, GCP).
- Experience developing and deploying ML models in production environments.
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