About TrueMoney
TrueMoney is a leading international fintech brand providing innovative payment and financial services across seven countries in Southeast Asia. With its user-friendly digital platform, extensive agent network, and comprehensive offline and online services, TrueMoney empowers millions of users to make easy, secure payments and enjoy a more convenient financial lifestyle.
Since its establishment, TrueMoney has grown to become the most popular digital financial application in the region, playing a central role in expanding access to financial services and improving quality of life for individuals and MSME communities.
TrueMoney is part of Ascend Money, a regional digital financial services company founded in 2013, which reached a major milestone by becoming Thailand's first fintech unicorn in 2021.
Key Responsibilities:
- Drive the end-to-end machine learning lifecycle, including data exploration, feature engineering, model training, validation, and deployment into production environments.
- Design and implement AI-powered chatbot solutions using RAG architecture, including:
- Knowledge ingestion (basic KM structuring, chunking, metadata)
- Embedding and retrieval using vector databases (e.g., Chroma, Pinecone, FAISS)
- LLM-based response generation with prompt design and basic guardrails
- Develop and maintain intent classification and routing logic (hybrid rule-based + ML) to support conversational AI use cases
- Work cross-functionally with Product, Engineering, and Business teams to translate business requirements into scalable AI/ML solutions
- Contribute to personalization and recommendation models (e.g., next-best-offer, campaign targeting) in financial services
- Implement and maintain best practices for model lifecycle management, including: Model versioning
- Basic monitoring (model performance, chatbot response quality)
- Support for retraining pipelines
- Support the Data Science Manager in exploring and applying emerging technologies such as Generative AI and LLM-based applications
- Ensure models comply with internal standards and regulatory requirements, including data privacy and basic explain ability
Qualifications:
- 4–6+ years of experience in data science or machine learning
- Proven experience deploying machine learning models into production in real-world business environments
- Hands-on experience with RAG-based applications or LLM-powered systems (e.g., chatbot, search, or Q&A systems)
- Familiarity with: Vector databases (e.g., Chroma, FAISS, Pinecone) LLM frameworks (e.g., LangChain, LlamaIndex)
- Prompt engineering and context handling
- Solid understanding of statistical modeling and machine learning algorithms
- Working knowledge of reinforcement learning concepts (e.g., bandits, basic policy optimization)
- Proficiency in Python and SQL
- Experience with cloud platforms (AWS or GCP) and basic MLOps tools (e.g., MLflow) is preferred
- Experience in financial services (e.g., lending, insurance, digital banking) is a plus
- Strong communication skills with the ability to explain technical concepts to non-technical stakeholders
- Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics) is preferred
