The Fixed Income Reference  team collects, manages, and supplies content related to Bonds . This content is used extensively across by clients and beyond Refinitiv/LSEG and is a critical asset to a wide range of Refinitiv/LSEG products.

 

The role of the Data Scientist is to:

Design and develop new methodologies, quantitative models, analysis and commentary (as relevant) that enhance existing or new processes.  Maintain existing product performance and independent analysis standards.  Internally, work with various departments to grow new ideas and expand the scope of existing products.

 

Essential Day-to-Day Responsibilities:

• Data handling, data processing and programming
• Develop automated solutions for sourcing/loading Fixed Income Reference data into Database
• Analyze Fixed Income Reference content to establish patterns/trends
• Develop, improve and run quantitative models
• Generate solutions through AI & machine learning for core processes
• Work with business, content and product groups for large-scale analytics problems
• Build POCs, visualizations and pipeline tools for product design and development
• Work with development groups for deployment of analytics solutions
• Interact with other internal teams as needed, with supervision.    
• Work closely with analytics and content 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 that align with product and organizational goals. 
• Support communication of project progress, insights, and outcomes to product, engineering, and business stakeholders. 
• Stay current with emerging technologies, modelling techniques, and market trends in financial analytics to inform continual improvement. 
• Build, train, test, and validate machine learning and deep learning models, including NLP, LLMs, and Retrieval‑Augmented Generation (RAG). 
• Develop evaluation frameworks and metrics to assess automation and AI solutions against business requirements. 
• Apply web scraping, crawling, entity extraction, and advanced pre‑/post‑processing techniques to prepare structured, semi‑structured, and unstructured data (including PDFs and scanned documents). 
• Build and deploy data and ML solutions on cloud platforms (AWS or similar), following MLOps and CI/CD best practices. 

 

Knowledge & Skills:

• Understanding of algorithms, model building, and evaluation
• Strong understanding of machine learning algorithms
• Knowledge of neural networks and deep learning frameworks
• Extensive experience using Python or R or equivalent
• Skills in transforming and preparing data for model training
• Proficient in using tools and libraries such as scikit-learn, numpy, pandas and jupyter
• Solid relational database skills
• Solid understanding of statistics and statistical language such as R
• Ability to handle large quantities of data
• Narrate stories (to technical and mostly non-technical audience) about our content and processes by data analysis and visualization
• Strong written, communication and presentation skills.  Able to respond and present work to peers, senior management and other stakeholders

 

Qualification & Experience:

• Higher education in Statistics, Mathematics or Engineering in Computer Science with Data science certification
• 3–5 years of experience in data science, analytics, or statistical modelling roles. 
• Solid grounding in data analytics, feature engineering, predictive modelling, NLP, LLMs, and RAG workflows. 
• Strong proficiency in Python and commonly used data science libraries (Pandas, NumPy, Scikit‑learn, etc.), with hands‑on model development experience. 
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Experience with Git and CI/CD pipelines (GitLab/GitHub runners). 
• 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 (AWS/Azure). 

Career Stage:

Senior Associate

Compensation Information:

LSEG is committed to offering competitive Compensation and Benefits. The anticipated annual gross base salary for this position is between 142,900 zł - 226,100 zł. Please be aware base salary ranges may vary by geographic location. In addition to our offered base salary, this role is eligible for our Annual Bonus Plan (”bonus plan”). Target Bonus % will be commensurate with role level and posted career stage. Individual salary will be reflective of job-related knowledge, skills and equivalent experience.

Benefits Information:

LSEG roles (excluding internships) are typically eligible for inclusion in our LSEG Benefits program. To view the benefits available for the role you're applying for, please click here. This document provides a list of benefits by country. Simply click on the country where the role is based to view the relevant details. If you have specific questions or would like further details, these can be discussed during your interview.

London Stock Exchange Group (LSEG) Information:

Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.

LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth.

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