Data Scientist
LSEG is hiring a Data Scientist. Level rates it ; you can apply on Level.
AI in this role
Data Scientist focused on building, training, and validating machine learning models, NLP solutions, and RAG workflows within emerging technologies.
The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterprise‑ready solutions for D&A Operations. In this role, the individual partners with Operations and Technology teams to design data‑driven 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, high‑impact solutions.
Role, Responsibilities & Key Accountabilities:
- Contribute to the development of 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, integrating multiple tools and services into coherent, efficient workflows.
- Work closely 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 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.
Required Skills:
- 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).
- Experience with commercial or open‑source data management tools.
- Strong analytical, problem‑solving, and prioritization skills.
- Effective communication skills with the ability to clearly articulate technical and conceptual ideas to both technical and non‑technical audiences.
- Ability to set and uphold coding standards.
- Ability to collaborate across teams and contribute to strategy execution through strong technical insight.
- Hands-on experience with leading commercial or open-source data management tools.
- Ability to diagnose and resolve complex technical and business problems using deep analytical and industry knowledge
- Broad business awareness and understanding of relevant internal business relationships.
Preferred
- 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.
Education:
- 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
Career Stage:
Senior AssociateLondon 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.
Our purpose is the foundation on which our culture is built. Our values of Integrity, Partnership, Excellence and Change underpin our purpose and set the standard for everything we do, every day. They go to the heart of who we are and guide our decision making and everyday actions.
Working with us means that you will be part of a dynamic organisation of 25,000 people across 65 countries. However, we will value your individuality and enable you to bring your true self to work so you can help enrich our diverse workforce.
We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone’s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
You will be part of a collaborative and creative culture where we encourage new ideas. We are committed to sustainability across our global business and we are proud to partner with our customers to help them meet their sustainability objectives. Our charity, the LSEG Foundation provides charitable grants to community groups that help people access economic opportunities and build a secure future with financial independence. Colleagues can get involved through fundraising and volunteering.
LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
Please take a moment to read this privacy notice carefully, as it describes what personal information London Stock Exchange Group (LSEG) (we) may hold about you, what it’s used for, and how it’s obtained, your rights and how to contact us as a data subject.
If you are submitting as a Recruitment Agency Partner, it is essential and your responsibility to ensure that candidates applying to LSEG are aware of this privacy notice.
How we rate this
Data Scientist at LSEG rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $169k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Tell me about a project where data science was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, ML Ops, NLP, Data Science, and Deep learning. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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