Business Graduate Associate Hiring at LSEG Bangalore — 2026
LSEG ( London Stock Exchange )The Business Graduate Associate hiring at LSEG in Bangalore is a 12-month graduate programme inside the Data & Analytics division, where you learn how financial data is turned into products that customers pay for. You join on a permanent contract and are placed in Data & Analytics from day one. The programme mixes learning (financial markets, data, analytics, AI, business intelligence, product management) with real project work, so it suits someone who likes both numbers and business problems rather than pure coding.
🎓 Who can apply.
You must have completed your studies in 2026, or be a final-year undergraduate or Master’s student who will finish all course requirements before summer 2027. That means the 2026 and 2027 batches are both eligible. LSEG states a preference for an Engineering + Finance combination, and welcomes Data Science, AI, Data Analytics, Business Analytics, Computer Science and other quantitative degrees.
Snapshot
| Role | Business Graduate Associate (Data & Analytics) |
|---|---|
| Company | London Stock Exchange Group (LSEG) |
| Division | Data & Analytics |
| Location | IND-BLR-Divyasree Technopolis, Bengaluru |
| Employment Type | Full-time, permanent, 12-month graduate programme |
| Eligibility | Completed studies in 2026, or final-year UG/Master’s finishing before summer 2027 |
| Preferred Background | Engineering + Finance; Data Science, AI, Analytics, CS or related quantitative degrees |
| Key Skills | SQL, introductory Python, financial markets basics, product and customer thinking |
| Apply By | 10 October 2026 |
| Salary | One third-party guide quotes ₹12–18 LPA for comparable technology graduate roles. Not published by LSEG |
| Requisition ID | R0123974 |
What You’d Do and Learn
- Build base skills: foundations in data, analytics, AI and business intelligence over the 12 months.
- Learn the domain: financial markets, customers and how LSEG makes money.
- Think like a product team: connect customer needs, data and business outcomes, which LSEG calls a “product-led” approach.
- Work across teams: join projects with colleagues from different functions to define problems and shape solutions.
- Grow beyond the desk: professional development sessions, CSR and volunteering, and a global Early Careers community.
Skills: What’s Required and What’s Preferred
- Listed as expected: basic understanding of financial markets, asset classes and corporate finance; practical SQL for querying, filtering and aggregating data across multiple tables; introductory Python for data analysis.
- Listed as what makes you stand out: interest in product management, curiosity about customers, interest in data science, analytics, AI or BI, strong communication and strategic thinking, and adaptability in a fast-paced setting.
Interview Prep
LSEG lists four stages: application, an immersive online assessment, a video interview, and an assessment centre. Public guides describe the online assessment as psychometric (numerical, logical and situational judgment) and the video interview as a recorded one. Confirm the exact format on your invite.
FAQ
- Q: Can 2027 graduates apply for this programme?
- Yes, if you are a final-year undergraduate or Master’s student who will complete all course requirements before summer 2027. Candidates who finished their studies in 2026 can also apply.
- Q: Do I need an engineering or finance degree?
- No degree is mandatory. LSEG prefers Engineering plus Finance and welcomes data, AI, analytics and quantitative degrees, but says other backgrounds can apply if they show strong interest and capability. SQL and basic financial knowledge are still worth preparing.
- Q: Is this a permanent job or an internship?
- It is a permanent role. The first 12 months are structured as a graduate programme, and graduates join on a permanent basis aligned to Data & Analytics from day one.
- Q: What is the salary for this programme?
- LSEG has not published it, and public data for this exact track is thin. Ask the recruiter for the compensation band before the assessment centre instead of relying on aggregator figures.
About LSEG ( London Stock Exchange )
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,...
View Company Profile →Top Interview Questions
Prepare with commonly asked questions for this role
Connect three things: your interest in data and how it becomes a product, a specific fact about LSEG (for example, that Data & Analytics grew out of Refinitiv and serves banks and asset managers), and one project or course where you used data to answer a question. Avoid generic lines about "a big brand."
Use the STAR structure: the situation, your task, the action you took, and the result. Pick a real disagreement about approach, show that you listened to the other view and used evidence to settle it, and end with what you learned. LSEG says it cares about how you achieve results, not only what you achieve.
JOIN the two tables on client ID, filter by a date condition, then GROUP BY client, SUM the trade value, and ORDER BY the total in descending order. Explain why you used an INNER JOIN or a LEFT JOIN, since the posting asks for work across multiple tables.
Name equities, fixed income, commodities, currencies and derivatives, with one line on each. Then link to the job: investors make decisions and manage risk from prices, ratings and company data, so a wrong or late data point can cost money. This is the reason a data business matters.
Start with the customer: ask what task they were trying to do, look at usage data to see where people drop off, check whether others report the same issue, then rank fixes by how many users they affect. This matches the posting's focus on understanding problems from the customer's side.