Data Scientist - AI (Collection)
Reads like a ML Engineer role: putting models into production.
Jakarta, ID
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s listing: Full-Time
From the employer’s posting
What you’ll work on
Full postingDesign and build LLM-powered collection chatbots and AI voice agents
Automate audit of desk collection calls and chats against SOPs using speech-to-text and LLMs
Develop predictive models for propensity-to-pay, contactability, and best channel/time
Design and analyze A/B tests on collection KPIs such as roll rate and recovery rate
Deploy and iterate on solutions with Engineering and Ops
From the employer’s posting
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
To develop and improve predictive models (e.g. propensity-to-pay, contactability, best channel/time to contact)
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
What you’ll bring
All qualificationsCore experience
- 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
Preferred experience
- Experience deploying models to production or working with MLOps tools (Airflow, Docker, cloud platforms) is a plus
Qualification wording
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
Experience deploying models to production or working with MLOps tools (Airflow, Docker, cloud platforms) is a plus
Education & alternatives
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
Tools in this posting
- Python
- SQL
- Docker
- Airflow
- LightGBM
- PyTorch
- scikit-learn
- XGBoost
Source — Tool mentions in context
- 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
- 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
Posting record
Report an errorActively hiringFirst seen by us 7 days ago, still listed, and not a repost of an earlier listing.
- Status in our records
- Live
- First seen by us
- Oct 16, 2026 · 7 days ago
- Recorded sightings
- 7
- Last seen by us
- Oct 23, 2026
- Employer says posted
- Oct 14, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on kredivo-group.breezy.hr. The employer’s form will show what is required.
Already applied? Track this application
Job description
Kredivo is hiring a Data Scientist – AI (Collection) in our Jakarta office to build AI-powered products that change how we collect payments from customers. This position will report to the Lead Data Scientist – Collection & SPG and will be accountable for designing, shipping and measuring AI solutions across the collection journey, from early reminders to late-stage recovery.
In this role you will also be responsible for driving AI transformation within Collection: building conversational AI (chatbots and voice bots/robocalls), automating desk collection audit processes, and applying LLMs to collection workflows. In addition, you will be responsible for proving business impact through rigorous experiments and working closely with Collection Ops, Product and Engineering to put models into production.
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
Source details
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- Location & working pattern
Jakarta, ID
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