Junior Data Scientist

Bank OCBC NISP; Jakarta Pusat; On-site; IDR 78,120,000 - 133,920,000; Full time

Job summary

Bank OCBC NISP is seeking a quantitative fresh graduate to join our AI/ML team. You will build and ship LLM systems and workflows used by bankers and customers, with hands-on work in Python, vLLM, and fine-tuning.

Responsibilities include evaluating AI via offline tests, A/B studies, and live monitoring, and collaborating with Product, Risk, and Compliance to apply data science to real banking scenarios including credit risk and regulatory compliance.

Qualifications

• Fresh graduate in a quantitative field such as Statistics, Computer Science, Math, Engineering or Physics.
• Strong Python fundamentals.
• Experience building something with LLMs (thesis, internship, side project or hackathon) and be able to explain its objective.

Responsibilities

• Build and ship LLM systems and workflows to production for bankers and customers.
• Learn to evaluate AI properly: offline evals, A/B tests, live monitoring.
• Work hands-on with Python, vLLM, prompt optimization, and fine-tuning.
• Collaborate with Product, Risk, and Compliance to apply data science in real banking scenarios including credit risk and regulatory compliance.

Skills

Python fundamentals

LLM projects

Explainable objectives

Education

Bachelor’s or Master’s in quantitative field

Tools

vLLM

LoRA/QLoRA

prompt optimization frameworks

Job description

What you'll do:

• Build and ship LLM systems and workflows to production, where real bankers and customers use them
• Learn to evaluate AI properly: offline evals, A/B tests, live monitoring
• Work hands‑on with Python, vLLM, prompt optimization, and fine‑tuning
• Work alongside experienced teams in Product, Risk, and Compliance to apply data science techniques to real‑world banking scenarios, including credit risk and regulatory compliance.

We're looking for curious and eager-to-learn candidates who are:

• Fresh graduate (Bachelor's or Master's) in a quantitative field: Statistics, Computer Science, Math, Engineering, Physics
• Strong Python fundamentals
• Have experience on building something with LLMs (through Thesis, internship, side project, or hackathon) and can explain the concept and its objective in a simple way

Bonus points (not required):

• Exposure to vLLM, LoRA/QLoRA fine‑tuning, or prompt optimization frameworks like GEPA
• Interest in financial data and regulation