Senior/Lead Data Scientist (Applied AI Research)

What you’ll Do:

• Collaborate closely with engineering teams to design and deploy end-to-end LLM applications that drive measurable business impact. 
• Implement scalable LLMOps workflows to streamline model training, deployment, monitoring, and ongoing optimization in production.
• Evaluate, test, and benchmark frontier and open-source LLMs to identify high-value applications across our product ecosystem.
• Adapt and fine-tune pre-trained models using domain-specific datasets and modern optimization techniques.
• Establish robust evaluation frameworks, testing harnesses, and monitoring infrastructure to ensure AI models optimize for accuracy, latency, safety, and cost efficiency.
• Stay at the forefront of AI research, continuously evaluating emerging tools, architectures, and methodologies to enhance engineering practices.

What you’ll Need:

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Analytics, Statistics, or a related quantitative field.
• 2–5 years of experience in data science, machine learning, or applied research.
• Solid foundation in statistics, probability, machine learning algorithms, and large language models (LLMs).
• Hands-on experience with some modern AI tooling and frameworks (e.g., vLLM, LangChain, or similar execution engines).
• Strong problem-solving skills, strong sense of ownership, and a passion for translating experimental research into production-grade systems.
• Excellent communication skills with both technical and non-technical stakeholders, with a proven ability to build and foster collaborative relationships.
• Ability to thrive in a dynamic, fast-paced environment and apply disciplined risk control to deliver high standards under constraints.
• Fluency in both English and Thai.

It’d be Great if you have: (if any) 

• Demonstrated experience deploying and optimizing LLM serving architectures for high-concurrency, low-latency production applications.
• Extensive hands-on experience training, post-training, fine-tuning (e.g., LoRA, QLoRA), and aligning (e.g., RLHF, DPO) large language or multimodal models.
• Familiarity with modern Retrieval-Augmented Generation (RAG) frameworks, vector databases (e.g., Pinecone, Milvus, Qdrant), and advanced retrieval techniques.
• Experience building agentic workflows, function calling pipelines, or multi-agent orchestration systems.
• Proven track record of publishing research papers at top-tier AI/ML conferences or journals.