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LSEG

Global financial market infrastructure and data

Lead Data Scientist

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • Eight to twelve years of experience in data science or related analytics and statistical modeling roles.
  • Own and drive end-to-end AI and machine learning solutions, from problem definition and experimentation through production deployment and continuous optimization.
  • Define and lead technical strategy, including model selection, evaluation metrics, and trade-offs across performance, scalability, and constraints.
  • Design and deliver advanced AI solutions, including natural language processing, large language models, generative AI, and retrieval-augmented generation, aligned with business outcomes.
  • Act as the primary technical authority, guiding data scientists and machine learning engineers while proactively identifying and resolving risks.
  • Establish and approve success criteria, including model evaluation metrics, quality benchmarks, and production-readiness standards.
  • Ensure responsible AI compliance, including governance, explainability, documentation, and audit readiness.
  • Collaborate with engineering teams to enable secure, scalable, and reliable production deployments using MLOps and CI/CD best practices.
  • Lead and mentor senior data scientists, driving full-stack data science delivery and fostering problem-solving and algorithmic thinking.
  • Apply expertise in Python, machine learning and deep learning frameworks (TensorFlow, PyTorch, and scikit-learn), and large-scale data processing.
  • Use foundations in statistics, data engineering, and cloud platforms (Azure or AWS) to deliver production-grade solutions.
  • Translate business requirements into technical solutions and influence cross-functional stakeholders to drive strategic outcomes.
  • 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 programming languages such as Python, R, and SQL.

Responsibilities

  • Lead, mentor, and develop a team of senior data scientists by providing technical direction, coaching, and performance feedback.
  • Drive end-to-end delivery of data science initiatives, provide hands-on technical leadership, remove roadblocks, and ensure high-quality execution.
  • Own and refine the product vision for an advanced data management and analytics framework covering data acquisition, transformation, quality, and workflow automation.
  • Define and optimize user experiences for financial analytics data pipelines by integrating multiple tools, data sources, and services into cohesive workflows.
  • Partner with domain experts and senior business stakeholders to identify high-value problems and shape actionable AI, machine learning, and platform strategies.
  • Translate business needs into technical specifications, solution designs, and clear success metrics.
  • Communicate project goals, insights, and outcomes to product, engineering, sales, proposition, support, and leadership stakeholders.
  • Influence cross-functional alignment by providing clear, data-driven recommendations and technical guidance.
  • Develop, deploy, and optimize production-grade AI models, including deep learning, natural language processing, large language models, and retrieval-augmented generation.
  • Evaluate third-party AI technologies, frameworks, and tools to inform build-versus-buy decisions and enhance platform capabilities.
  • Establish and enforce coding standards, quality controls, and reproducibility practices for robust machine learning development.
  • Lead model performance tuning, scalability improvements, and continuous enhancement cycles.
  • Apply data extraction techniques, including web scraping, crawling, entity recognition, and advanced pre-processing and post-processing.
  • Manage complex structured, semi-structured, and unstructured data, including financial documents, PDFs, and scanned inputs.
  • Collaborate with data engineering teams to ensure scalable and reliable data pipelines supporting analytical workflows.
  • Align delivery with MLOps workflows, CI/CD pipelines, and cloud-native deployment practices.
  • Lead scalable deployment of AI and machine learning solutions on AWS, Azure, or equivalent cloud environments.
  • Work with platform engineering teams to enhance monitoring, observability, and model lifecycle management.
  • Stay current with emerging technologies and trends in financial analytics, AI, natural language processing, cloud computing, and model engineering.
  • Promote innovation by encouraging experimentation, exploration of frontier techniques, and continuous skill development across the team.
  • Identify opportunities to improve frameworks, modeling practices, tools, and processes across the data science organization.

Desired Qualifications

  • Experience working within large investment banking or financial services organizations.
  • Experience defining enterprise AI governance, ethics guidelines, risk frameworks, or regulatory compliance programs.
  • Published research, patents, or conference presentations in AI, machine learning, natural language processing, or related fields.
  • Experience leading multi-team or matrixed delivery organizations across regions and time zones.
  • Industry reputation as a technical expert or AI thought leader.

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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