AI Data Scientist – Collections & Fintech Impact

Kredivo; Jakarta Pusat; On-site; IDR 240,000,000 - 360,000,000; Full time

Job summary

Kredivo in Jakarta seeks a Data Scientist – AI (Collection) to design, ship and measure AI solutions across the collection journey. You will build LLM-powered assistants, automate desk collection analysis, and develop predictive models with rigorous experiments.

Collaborating with Collection Ops, Product and Engineering, you will push AI transformations from early reminders to late-stage recovery, applying RAG, prompt engineering, and MLOps practices to production systems.

Qualifications

• Bachelor's or Master's in a quantitative field.
• 2+ years in data science or ML, preferably fintech/collections.
• Strong Python and SQL; hands-on with ML libraries.
• Hands-on with LLMs: prompt engineering, RAG, agents and APIs.
• Exposure to conversational AI or voice tech is a plus.
• Experience with experimentation and statistics (A/B testing).
• Production deployment or MLOps exposure is a plus.
• Fluent in Bahasa Indonesia and good English.
• Product-minded and hands-on: shipping, measuring and iterating.

Responsibilities

• Design and build LLM-powered collection assistants (WhatsApp chatbots).
• Audit and analyze desk collection calls/chats using STT and LLMs.
• Develop predictive models (propensity-to-pay, contactability).
• Design and analyze A/B tests to measure KPI impact.
• Collaborate with Engineering and Ops to deploy and scale solutions.

Skills

Python

SQL

ML libs

LLMs

A/B testing

MLOps

Bahasa Indonesia

English

Education

CS/Math/Engineering degree

Tools

scikit-learn

XGBoost/LightGBM

PyTorch

Airflow

Docker

Job description

Kredivo in Jakarta seeks a Data Scientist – AI (Collection) to design, ship and measure AI solutions across the collection journey. You will build LLM-powered assistants, automate desk collection analysis, and develop predictive models with rigorous experiments.

Collaborating with Collection Ops, Product and Engineering, you will push AI transformations from early reminders to late-stage recovery, applying RAG, prompt engineering, and MLOps practices to production systems.