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REQUIREMENT_ID_1463 β€’ 3-DAY_ACTIVE_POLICY

Junior Associate - LEDH

Company
Company Swiss Re
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary β‚Ή10 LPA
Location
Location Bangalore, Karnataka
Posted Date
Posted Date Yesterday
MS Excel data validation data cleaning analytical thinking attention to detail stakeholder management basic Python SQL Power BI communication skills
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Aptitude Practice Questions Open Resource β†—
Helps you prepare for the quantitative and logical reasoning sections of Swiss Re's online assessment.
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Company Interview Preparation Guide Open Resource β†—
Provides insights into typical interview questions and company‑specific topics for Swiss Re and similar firms.
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General Interview Preparation Resources Open Resource β†—
Covers behavioural and HR interview strategies useful for the final round at Swiss Re.
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Algorithm & Data Structure Practice Open Resource β†—
Strengthens problem‑solving skills that may be tested in technical rounds involving Python or SQL.

Bachelor's or Master's degree in Engineering, Commerce, Mathematics, Statistics, Management, Data Analytics or related discipline; Minimum 60% aggregate (or CGPA 6.5/10); Fresh graduates or candidates with 1‑2 years of relevant experience; No active backlogs at the time of joining; Open to all graduating batches.

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Round 1: Online aptitude test (quantitative, logical reasoning, English)
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Round 2: Technical interview focusing on data handling, Excel & basic programming
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Round 3: HR interview covering fit, motivation and cultural alignment
Swiss Re is a global leader in reinsurance, insurance and risk‑transfer solutions, operating in more than 25 countries with a workforce of over 15,000 professionals. The company’s mission is to make the world more resilient by helping clients manage complex risks ranging from natural catastrophes to cyber threats. With a strong focus on innovation, sustainability and inclusive culture, Swiss Re offers a dynamic environment where fresh talent can grow alongside industry veterans. The Junior Associate – LEDH role sits within the Loss & Exposure Data Handling team of the Corporate Solutions division. This team is the backbone of the underwriting and actuarial workflow, turning raw loss, exposure and submission data into clean, validated information that drives pricing, risk assessment and portfolio decisions for Property & Casualty lines. As a junior associate you will be at the intersection of operations and analytics, supporting global stakeholders while sharpening your data‑management skills. Key responsibilities include: 1. Capture, validate and process loss, exposure and submission data in line with SOPs and business rules. 2. Meet daily/weekly Turn‑around Times (TAT) and Service Level Agreements (SLA) for high‑volume processing. 3. Extract, cleanse and analyse large structured and unstructured datasets using Excel and basic scripting. 4. Identify data quality issues such as missing fields, duplicates or inconsistencies and work with underwriters to resolve them. 5. Produce accurate outputs for pricing, experience rating and risk‑assessment models. 6. Contribute to ad‑hoc reporting, portfolio reviews and analytical requests from actuarial teams. 7. Maintain proactive communication with underwriters, actuaries and cross‑functional teams across regions, documenting action items and follow‑ups. 8. Participate in continuous‑improvement initiatives, suggesting automation ideas and supporting projects using Excel, Python or GenAI tools. 9. Assist in Quality Control (QC) reviews, User Acceptance Testing (UAT) and rollout of new processes. 10. Uphold a customer‑centric mindset, ensuring professional communication and timely escalation of risks. Tech stack: Advanced MS Excel (pivot tables, VLOOKUP, macros), basic Python for data cleaning, SQL basics, MS Access, Power BI for visualisation, and emerging GenAI tools for workflow automation. Growth path: Successful performers can progress to Senior Associate – LEDH, then to Lead Analyst or Business Analyst roles, eventually moving into underwriting, actuarial support or data‑science tracks within Swiss Re’s global operations. Why join Swiss Re? The company offers exposure to world‑class insurance data, mentorship from seasoned underwriters and actuaries, and a hybrid work model that balances office collaboration with remote flexibility. Employees benefit from structured learning programs, competitive compensation, and a culture that values diversity, sustainability and continuous innovation.

Swiss Re β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
Swiss Re Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Swiss Re and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (MS Excel, data validation, data cleaning, analytical thinking, attention to detail, stakeholder management, basic Python, SQL, Power BI, communication skills) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Swiss Re Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer β–Ό
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer β–Ό
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer β–Ό
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer β–Ό
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join Swiss Re as a Junior Associate - LEDH?
Preparation Tip: Highlight Swiss Re's market reputation, recent tech innovations, and how your skills in MS Excel directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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