Senior Enterprise Data Scientist — AI, NLP & RAG

LSEG; Hong Kong; On-site; HKD 900,000 - 1,500,000; Full time

Benefits offered by this job

Healthcare

Retirement planning

Paid volunteering days

Wellbeing initiatives

Job summary

London Stock Exchange Group (LSEG) is seeking an experienced Enterprise Data Scientist to drive data-driven insights and AI-powered customer solutions across Asia-Pacific. You will build POCs and production-grade agentic AI systems, collaborate with cross-functional teams, and integrate third-party technologies with client workloads.

You will lead complex engagements, design scalable NLP/ML architectures, and implement Retrieval-Augmented Generation pipelines with robust MLOps, governance, and

Qualifications

• 10+ years in data science with AI/NLP focus.
• Experience with enterprise-grade AI solutions and customer engagements.
• Proven ability to design and deploy ML/NLP systems in production.

Responsibilities

• Lead and execute complex customer engagements across APAC.
• Co-create AI/NLP solutions addressing customer needs.
• Present advanced data science concepts to stakeholders clearly.
• Manage internal/external stakeholder requirements and translate to actionable designs.
• Oversee PoCs and ensure alignment with client workflows.

Skills

AI/ML/NLP expertise

LLMOps

Python

Cloud platforms

Stakeholder management

Education

Bachelor’s or Master’s in CS/Engineering/DS

PhD in a related field (plus)

Tools

LangGraph

AutoGen

CrewAI

LangSmith

RAGAS

Arize

Pinecone

Weaviate

PyTorch

Hugging Face

Job description

London Stock Exchange Group (LSEG) is seeking an experienced Enterprise Data Scientist to drive data-driven insights and AI-powered customer solutions across Asia-Pacific. You will build POCs and production-grade agentic AI systems, collaborate with cross-functional teams, and integrate third-party technologies with client workloads.

You will lead complex engagements, design scalable NLP/ML architectures, and implement Retrieval-Augmented Generation pipelines with robust MLOps, governance, and