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LSEG

Global financial market infrastructure and data

Enterprise Data Scientist

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • At least 10 years of experience in data science or a related field, focused on artificial intelligence, machine learning, and natural language processing.
  • A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field; a Ph.D. in a relevant field is a plus.
  • Expertise in natural language processing and generative artificial intelligence, with deep understanding of transformer architectures including BERT and T5 and current frontier and open-weight models such as GPT-5.x, Claude 4/5, Gemini 2.x/3.x, Llama 4, DeepSeek, and Qwen.
  • Hands-on experience building agentic artificial intelligence systems, including multi-agent orchestration with LangGraph, AutoGen, or CrewAI, tool/function calling, and Model Context Protocol integration.
  • Practical experience with Retrieval-Augmented Generation, including chunking strategies, embedding models, hybrid search, and retrieval evaluation.
  • Experience fine-tuning and adapting large models using LoRA/QLoRA, parameter-efficient fine-tuning, quantization, and distillation, and applying LLMOps practices for prompt evaluation, guardrails, hallucination testing, and observability tools such as LangSmith, RAGAS, and Arize.
  • Awareness of responsible artificial intelligence and governance requirements, including model risk management and emerging regulation such as the EU AI Act as applicable to financial services.
  • Extensive experience with machine learning and deep learning frameworks including PyTorch, TensorFlow, Hugging Face, and JAX for natural language processing, multimodal vision-language, and other advanced artificial intelligence tasks.
  • Deep knowledge of AWS, Google Cloud Platform, and Azure and their use in data science workflows, particularly for deploying machine learning models at scale.
  • Expertise in Python and advanced knowledge of data science and machine learning libraries including Pandas, NumPy, SciPy, scikit-learn, and spaCy, as well as Hugging Face Transformers, Datasets, and NLTK for model training, fine-tuning, and data preprocessing.
  • Strong programming skills in Python, R, and SQL, with advanced proficiency in large-scale data handling using Apache Spark; cloud-native NoSQL databases such as MongoDB, Cassandra, and DynamoDB; search engines such as Elasticsearch; and vector databases such as Pinecone and Weaviate.
  • Hands-on experience with data ingestion, data wrangling, and data pipeline orchestration using Apache Kafka, Apache Spark, and Airflow, and distributed computing frameworks such as Dask and Ray.
  • Experience with ensemble learning, deep reinforcement learning, transfer learning, and deploying large pre-trained models for real-time inference and production.
  • Ability to design, prototype, and deploy natural language processing models for information retrieval, sentiment analysis, chatbots, and question-answering systems.
  • Strong communication skills and ability to explain complex technical concepts to technical and non-technical stakeholders.
  • Proven experience in customer-facing roles is highly valued, particularly in enterprise technology or financial sectors, ideally serving customers across Asia-Pacific.
  • Familiarity with artificial-intelligence-powered product development in finance, healthcare, or e-commerce.
  • Experience with data visualization tools such as Tableau, Power BI, or Plotly.
  • Knowledge of financial regulatory requirements, including experience working with financial data feeds and APIs; familiarity with the Singapore regulatory environment, including MAS, is a plus.

Responsibilities

  • Lead and execute complex customer engagements across Asia-Pacific using expertise in artificial intelligence, machine learning, generative artificial intelligence, and natural language processing, including building proof-of-concepts, agentic AI workflows, integrations, and deployments with customer workflows.
  • Apply technical, product, and data science expertise to co-create solutions for customer needs, including ideation, clarification, technical design, and documentation.
  • Lead detailed customer presentations for complex technical propositions, explaining advanced data science concepts and AI/ML solutions accessibly.
  • Manage relationships with internal and external stakeholders, capturing, refining, and translating project and customer-specific technical requirements into actionable solutions.
  • Oversee and contribute to proof-of-concept development, ensuring integration with customer workflows and systems.
  • Lead the technical design and implementation of artificial intelligence and machine learning solutions integrated with existing client infrastructure.
  • Drive adoption of advanced data science and artificial intelligence technologies to deliver high-value solutions.
  • Develop and present strategies for scaling artificial intelligence solutions using Azure, AWS, and Google Cloud Platform for production-ready deployments.
  • Design and implement Retrieval-Augmented Generation pipelines and agentic artificial intelligence systems that orchestrate multiple tools and models to solve customer problems.
  • Establish LLMOps practices, including evaluation, guardrails, and observability, for safe, reliable, and responsible deployment of generative AI solutions in line with regulatory expectations.
  • Lead and mentor junior team members across the Singapore and broader Asia-Pacific team, supporting continuous learning and technical growth.

About the Company

LSEG

Global financial market infrastructure and data

LSEG provides global financial market infrastructure and data across the full value chain. It delivers data, analytics, and risk-management tools, benchmarks via FTSE Russell, regulatory and financial crime risk services, and trading, clearing, and settlement through its Capital Markets and Post Trade divisions, typically accessed via subscriptions, licensing, and fees. The company differentiates itself by offering an integrated, end-to-end platform that covers pre-trade data and analytics to post-trade processing with a broad, global footprint. Its goal is to grow over the long term by leveraging its diversified portfolio and international reach to create value for shareholders and customers.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1801

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