Enterprise Data Scientist

About the role

The Enterprise Data Scientist will lead AI‑driven initiatives across the Asia‑Pacific region, delivering end‑to‑end data science solutions that address complex customer needs. This senior position combines technical expertise, product design, and client engagement to build production‑grade AI systems, generative AI, and NLP applications.

Key responsibilities

• Lead and execute complex customer engagements across APAC, leveraging AI, ML, Generative AI and NLP to build POCs, agentic AI workflows, integrations and deployments.
• Apply technical, product and data‑science expertise to co‑create solutions, covering ideation, design, documentation and delivery.
• Present detailed technical proposals to customers, translating advanced data‑science concepts into accessible language.
• Manage relationships with internal and external stakeholders, capturing and translating requirements into actionable solutions.
• Oversee development and integration of Proof of Concepts within customer workflows and systems.
• Design and implement AI/ML solutions that integrate with existing client infrastructure.
• Drive adoption of advanced data‑science and AI technologies, creating scaling strategies using cloud platforms (Azure, AWS, GCP).
• Design Retrieval‑Augmented Generation (RAG) pipelines and agentic AI systems that orchestrate multiple tools and models.
• Establish LLMOps practices—evaluation, guardrails, observability—to ensure safe, responsible generative AI deployment.
• Lead and mentor junior team members across Singapore and the broader APAC team.

Required profile

• 10+ years of experience in data science or a related field, with a focus on AI, machine learning and NLP, preferably in a senior technical or leadership role.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or related field; Ph.D. a plus.
• Deep expertise in NLP and Generative AI, including LLM architectures such as BERT, T5 and frontier models (GPT‑5.x, Claude 4/5, Gemini 2.x/3.x, Llama 4, DeepSeek, Qwen).
• Hands‑on experience building agentic AI systems, multi‑agent orchestration (LangGraph, AutoGen, CrewAI) and Model Context Protocol integration.
• Practical experience with Retrieval‑Augmented Generation, fine‑tuning large models (LoRA/QLoRA, PEFT, quantization, distillation) and LLMOps tools.
• Awareness of responsible AI, governance and regulatory requirements (e.g., EU AI Act) relevant to financial services.
• Extensive experience with advanced ML/DL frameworks (PyTorch, TensorFlow, Hugging Face, JAX) and cloud services (AWS, Azure, GCP) for scalable deployments.

Required skills

• Python
• R
• SQL
• PyTorch
• TensorFlow
• JAX
• Hugging Face Transformers
• Apache Spark
• Apache Kafka
• Airflow
• Dask
• Ray
• LangGraph
• AutoGen
• CrewAI
• Model Context Protocol
• Retrieval‑Augmented Generation techniques
• LoRA/QLoRA, PEFT, quantization, distillation
• LLMOps tools (LangSmith, RAGAS, Arize)
• AWS
• Azure
• GCP
• MongoDB
• Cassandra
• DynamoDB
• Elasticsearch
• Pinecone
• Weaviate
• Tableau
• Power BI
• Plotly