Lead Data Scientist

London Stock Exchange Group (LSEG); Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

London Stock Exchange Group (LSEG) in Bengaluru seeks a senior data scientist lead to architect and drive enterprise AI/ML initiatives. You will mentor a team, deliver production-grade models, and shape the analytics platform strategy to unlock business value across finance domains.

The role emphasizes responsible AI governance, scalable deployments (MLOps), and collaboration with cross-functional teams to achieve measurable outcomes.

Qualifications

• 8-12 years of experience in data science or related analytics roles.
• Own and drive end-to-end AI/ML solutions from problem definition to production deployment.
• Define and lead technical strategy, including model selection, evaluation metrics, and tradeoffs across performance, scalability, and constraints.
• Design and deliver advanced AI solutions (including NLP, LLMs, GenAI, and RAG) aligned with business outcomes.
• Act as the primary technical authority, guiding data scientists and ML engineers while proactively identifying and resolving risks.
• Establish and sign off on success criteria, including model evaluation metrics and production readiness standards.
• Ensure Responsible AI governance, explainability, and audit readiness.
• Collaborate with engineering teams to enable secure, scalable production deployments using MLOps and CI/CD.

Responsibilities

• Lead, mentor, and develop a team of senior data scientists, providing technical direction, coaching, and performance feedback.
• Drive end-to-end delivery of data science initiatives, offering hands-on technical leadership and removing roadblocks.
• 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, integrating multiple tools, data sources, and services into cohesive workflows.
• Partner with domain experts and senior stakeholders to identify high-value problems and shape actionable AI, ML, 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 crossfunctional alignment by providing datadriven recommendations and technical guidance.

Skills

End-to-end AI/ML

Leadership

Python

TensorFlow

PyTorch

NLP

GenAI/RAG

MLOps

Azure/AWS

Data analytics

Statistical modelling

Communication

Education

Masters in Statistics/Math/CS with Data Science cert

Proficiency in Python, R, SQL

Tools

Scikit-learn

Spark

SQL databases

Git

Job description

• The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterpriseready solutions for D&A Operations.
• In this role, the individual partners with Operations and Technology teams to design datadriven solutions that deliver clear business and customer value.
• The position requires strong expertise in data analytics, NLP, deep learning, and data communication, along with the ability to learn financial content and core D&A business processes.
• The individual must stay current with emerging technologies and collaborate closely with operations groups and specialized machine learning teams to deliver scalable, highimpact solutions.

Role, Responsibilities & Key Accountabilities:

Strategic & Technical Leadership

• Lead, mentor, and develop a team of senior data scientists, providing technical direction, coaching, and performance feedback.
• Drive endtoend delivery of data science initiatives, offering handson technical leadership, removing roadblocks, and ensuring highquality 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, integrating multiple tools, data sources, and services into cohesive workflows.

Business & Stakeholder Engagement

• Partner with domain experts and senior business stakeholders to identify highvalue problems and shape actionable AI, ML, 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 crossfunctional alignment by providing clear, datadriven recommendations and technical guidance.

Advanced AI/ML Delivery

• Develop, deploy, and optimize productiongrade AI models including deep learning, NLP, LLMs, and RetrievalAugmented Generation (RAG).
• Evaluate thirdparty AI technologies, frameworks, and tools to guide buildvsbuy decisions that enhance platform capabilities.
• Establish and enforce coding standards, quality controls, and reproducibility practices for robust ML development.
• Lead model performance tuning, scalability improvements, and continuous enhancement cycles.

Data Engineering & Processing Expertise

• Apply deep expertise in data extraction techniques, including web scraping, crawling, entity recognition, and advanced pre/postprocessing.
• Manage complex structured, semistructured, and unstructured dataincluding financial documents, PDFs, and scanned inputs.
• Collaborate with data engineering teams to ensure scalable and reliable data pipelines that support highimpact analytical workflows.

Cloud, MLOps & Deployment Leadership

• Ensure strong alignment with MLOps workflows, CI/CD pipelines, and cloudnative deployment practices.
• Lead scalable deployment of AI/ML solutions on AWS, Azure, or equivalent cloud environments.
• Work closely with platform engineering teams to enhance monitoring, observability, and model lifecycle management.

Continuous Improvement & Innovation

• Stay current with emerging technologies and trends in financial analytics, AI, NLP, cloud computing, and model engineering.
• Promote a culture of 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.

Required Skills:

• 8-12 years of experience in data science or related analytics and statistical modelling roles.
• Own and drive endtoend AI/ML solutionsfrom problem definition and experimentation to production deployment and continuous optimization
• Define and lead technical strategy, including model selection, evaluation metrics, and tradeoffs across performance, scalability, and constraints
• Design and deliver advanced AI solutions (including NLP, LLMs, GenAI, and RAG) aligned with business outcomes
• Act as the primary technical authority, guiding data scientists and ML engineers while proactively identifying and resolving risks
• Establish and sign off on success criteria, including model evaluation metrics, 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 fullstack data science delivery and fostering strong problemsolving and algorithmic thinking
• Apply deep technical expertise in Python, ML/DL frameworks (TensorFlow, PyTorch, Scikitlearn), and largescale data processing
• Leverage strong foundations in statistics, data engineering, and cloud platforms (Azure/AWS) for production-grade solutions
• Translate business requirements into technical solutions and influence crossfunctional stakeholders to drive strategic outcomes

Preferred

• 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, ML, NLP, or related fields.
• Experience leading multiteam or matrixed delivery organizations across regions and time zones.
• Industry reputation as a technical expert or AI thought leader.

Education:

• Masters 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.