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Jakarta, ID
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About the job
- To design and build LLM-powered collection assistants (WhatsApp/in-app chatbots, AI voice agents/robocalls)
- To build automated audit and analysis of desk collection calls and chats, using speech-to-text and LLMs to check them against collection SOPs at scale and turn them into insights for Collection Ops
- To develop and improve predictive models (e.g. propensity-to-pay, contactability, best channel/time to contact) that decide who to contact, when and how
- To design and analyze A/B tests that measure impact on collection KPIs (roll rate, recovery rate, cost per collection)
- To work with Engineering and Ops to deploy, scale and iterate on solutions, and to make sure they follow our internal collection guidelines
About you
- Bachelor's or Master's in Computer Science, Statistics, Mathematics, Engineering or a related quantitative field
- 2+ years of experience in data science or ML, ideally in fintech, lending, collections or customer operations
- Strong Python and SQL; hands-on with ML libraries (scikit-learn, XGBoost/LightGBM, PyTorch or similar)
- Hands-on experience building with LLMs: prompt engineering, RAG, function calling/agents, and LLM APIs or open-source models
- Exposure to conversational AI or voice tech (chatbot frameworks, STT/TTS, voice bot platforms) is a strong plus
- Solid grounding in experimentation and statistics (A/B testing, causal inference basics)
- Experience deploying models to production or working with MLOps tools (Airflow, Docker, cloud platforms) is a plus
- Fluent in Bahasa Indonesia and good English; able to explain technical work to non-technical stakeholders
- Product-minded and hands-on: you like shipping, measuring and iterating quickly
- If this sounds like you, we would love to hear from you!
#LI-AL1
Job description provided by Kredivo Group.
View the original posting