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LSEG

Global financial market infrastructure and data

Data Scientist

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeTaguig, Metro Manila, PhilippinesNo salary listedIn-person

About the Job

Requirements

  • Three to five years of experience in data science, analytics, or statistical modelling roles.
  • A solid grounding in data analytics, feature engineering, predictive modelling, natural language processing, large language models, and Retrieval-Augmented Generation workflows.
  • Strong proficiency in Python and commonly used data science libraries, including Pandas, NumPy, and Scikit-learn, with hands-on model development experience.
  • Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
  • Experience with Git and continuous integration and continuous delivery pipelines, including GitLab or GitHub runners.
  • A strong understanding of statistics, relational databases, and statistical programming concepts.
  • Ability to work effectively with large and complex datasets.
  • Understanding of MLOps practices and experience with cloud environments such as AWS or Azure.
  • Experience with commercial or open-source data management tools.
  • Strong analytical, problem-solving, and prioritization skills.
  • Effective communication skills to articulate technical and conceptual ideas to technical and non-technical audiences.
  • Ability to set and uphold coding standards.
  • Ability to collaborate across teams and contribute to strategy execution through technical insight.
  • Ability to diagnose and resolve complex technical and business problems using analytical and industry knowledge.
  • Broad business awareness and understanding of relevant internal business relationships.
  • A master's degree in Statistics, Mathematics, or Computer Science with a Data Science certification, or an Engineering degree specializing in Data Science and Artificial Intelligence.
  • Proficiency in Python, R, and SQL.

Responsibilities

  • Contribute to developing an enterprise-grade data management framework, including data acquisition, cleaning, transformation, and workflow optimization.
  • Help design and refine user experiences for financial analytics data pipelines by integrating multiple tools and services into efficient workflows.
  • Work with analytics domain experts to understand business data requirements and translate them into platform components, models, or analytical workflows.
  • Collaborate with engineering and project delivery teams to implement data solutions aligned with product and organizational goals.
  • Communicate project progress, insights, and outcomes to product, engineering, and business stakeholders.
  • Stay current with emerging technologies, modelling techniques, and financial analytics market trends to inform continual improvement.
  • Build, train, test, and validate machine learning and deep learning models, including natural language processing, large language models, and Retrieval-Augmented Generation.
  • Develop evaluation frameworks and metrics to assess automation and artificial intelligence solutions against business requirements.
  • Apply web scraping, crawling, entity extraction, and advanced pre-processing and post-processing techniques to prepare structured, semi-structured, and unstructured data, including PDFs and scanned documents.
  • Build and deploy data and machine learning solutions on cloud platforms such as AWS, following MLOps and continuous integration and continuous delivery best practices.

Desired Qualifications

  • Experience working in financial services or investment banking.
  • Familiarity with Refinitiv or LSEG datasets, tools, or workflows.
  • Experience working with distributed or cross-location teams.

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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