Senior Data Scientist

About the Company

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.

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), and 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), and 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 explainability.

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) and 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.