Senior 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, India seeks a senior data scientist to own the data management framework, drive NLP/LLM/RAG initiatives, and build end-to-end AI/ML solutions for financial analytics. You will partner with Operations and Technology to translate business data needs into scalable platform strategies.

You will lead cross-functional teams to deploy production-grade models, optimize performance, and communicate progress to stakeholders, staying ahead of market trends.

Qualifications

• Strong foundation in data analytics, feature engineering, NLP, predictive modelling, LLMs, and RAG workflows.
• Expertise in Python and core ML/DL libraries (TensorFlow, PyTorch, Scikitlearn), with demonstrated experience in developing and productionizing AI models.
• Proven ability to apply strong statistical analysis skills to model development and evaluation.
• Experience with Git-based version control and CI/CD pipelines. Strong understanding of relational databases and large-scale data processing.
• Familiarity with MLOps principles and deploying solutions on cloud platforms (AWS and/or Azure).
• Hands-on experience with commercial or open-source data management tools.

Responsibilities

• Own the product vision for an industry-leading data management framework, covering data acquisition, cleaning, transformation, and related workflows.
• Support Lead Data Scientist on end-to-end AI/ML lifecycle: problem definition, design, experimentation, deployment, and continuous improvement.
• Define an optimized user experience for financial analytics data pipelines, unifying multiple tools and services into an integrated workflow.
• Partner with analytics domain experts to understand business data needs and translate them into a practical platform and framework strategy.
• Lead the execution of this strategy in close collaboration with engineering and project delivery teams.
• Communicate product goals, progress, and achievements to product, engineering, business leaders, sales, proposition, support, and other internal stakeholders.
• Stay informed on financial analytics market trends to maintain a forward-looking, futureready strategy.
• Continuously learn and adapt to emerging technologies and techniques in financial analytics.
• Develop and deploy production-grade AI models and data-driven solutions aligned with strategic objectives.
• Tune and optimize AI models to improve accuracy, performance, and scalability.
• Evaluate third-party AI capabilities using both functional and nonfunctional criteria.
• Establish and enforce coding standards to ensure a maintainable, robust codebase.
• Collaborate with Software Engineers and cross-functional teams to define requirements, set direction, and shape development roadmaps.
• Hands-on experience in data science, including NLP, LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG).
• Skilled in training, testing, and validating deep learning models as well as classical machine learning models.
• Ability to design and implement evaluation frameworks for automation solutions based on business requirements.
• Experience building solutions and onboarding workloads on AWS or other cloud platforms.
• Expertise in web crawling and scraping techniques, entity identification and extraction, and advanced pre and post-processing methods.
• Proficient in processing structured, semistructured, and unstructured data from PDFs and scanned documents.

Skills

Data science

NLP

LLMs

RAG workflows

Python

TensorFlow

PyTorch

Scikit-learn

Git

CI/CD

AWS

Azure

MLOps

Data management tools

Communication

Stakeholder management

Education

Masters in Statistics, Mathematics, or Computer Science with a Data Science certification

Proficiency in Python, R, and SQL

Tools

Git

CI/CD pipelines

AWS

Azure

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:

• Own the product vision for an industryleading data management framework, covering data acquisition, cleaning, transformation, and related workflows.
• Support Lead Data Scientist on end-to-end AI/ML lifecycle: problem definition, design, experimentation, deployment, and continuous improvement.
• Define an optimized user experience for financial analytics data pipelines, unifying multiple tools and services into an integrated workflow.
• Partner with analytics domain experts to understand business data needs and translate them into a practical platform and framework strategy.
• Lead the execution of this strategy in close collaboration with engineering and project delivery teams.
• Communicate product goals, progress, and achievements to product, engineering, business leaders, sales, proposition, support, and other internal stakeholders.
• Stay informed on financial analytics market trends to maintain a forwardlooking, futureready strategy.
• Continuously learn and adapt to emerging technologies and techniques in financial analytics.
• Develop and deploy productiongrade AI models and datadriven solutions aligned with strategic objectives.
• Tune and optimize AI models to improve accuracy, performance, and scalability.
• Evaluate thirdparty AI capabilities using both functional and nonfunctional criteria.
• Establish and enforce coding standards to ensure a maintainable, robust codebase.
• Collaborate with Software Engineers and crossfunctional teams to define requirements, set direction, and shape development roadmaps.
• Handson experience in data science, including Natural Language Processing, Large Language Models, prompt engineering, and RetrievalAugmented Generation (RAG).
• Skilled in training, testing, and validating deep learning models as well as classical machine learning models.
• Ability to design and implement evaluation frameworks for automation solutions based on business requirements.
• Experience building solutions and onboarding workloads on AWS or other cloud platforms.
• Expertise in web crawling and scraping techniques, entity identification and extraction, and advanced pre and postprocessing methods using emerging technologies such as prompt engineering.
• Proficient in processing structured, semistructured, and unstructured data from PDFs and scanned documents.

Required Skills:

• 6-8 years of experience in data science, analytics, or statistical modelling roles.
• Strong foundation in data analytics, feature engineering, NLP, predictive modelling, LLMs, and RAG workflows.
• Expertise in Python and core ML/DL libraries (TensorFlow, PyTorch, Scikitlearn), with demonstrated experience in developing and productionizing AI models.
• Proven ability to apply strong statistical analysis skills, including probability and applied statistics, to model development and evaluation.
• Experience with Gitbased version control and CI/CD pipelines (GitLab/GitHub runners). Strong understanding of relational databases, largescale data processing, and statistical programming.
• Familiarity with MLOps principles and experience deploying solutions on cloud platforms (AWS and/or Azure).
• Handson experience with commercial or opensource data management tools.
• Excellent communication and presentation skills, capable of simplifying complex technical concepts for both technical and business audiences.
• Strong influencing and collaboration skills, supporting crossteam execution and alignment. Ability to diagnose and resolve complex technical and business problems using deep analytical and industry knowledge
• Ability to set and uphold coding standards.
• Hands-on experience with leading commercial or open-source data management tools.
• Excellent written, verbal, and presentation skills, with the ability to present to peers, senior management, and cross-functional partners.
• Broad business awareness and understanding of relevant internal business relationships.

Preferred

• Experience in large financial services or investment banking environments.
• Familiarity with Refinitiv or LSEG products and financial data workflows.
• Experience working with global or distributed teams.
• Comfort interacting with senior stakeholders, including executives and Csuite partners.

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 Python, R, and SQL.