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REQUIREMENT_ID_1354 • 3-DAY_ACTIVE_POLICY JD

Customer Support Executive - WFH English Hindi

Company
Company Teleperformance (TP)
Type
Opportunity Type Internship
Salary
Stipend / Salary 2-2.75 LPA
Location
Location Remote, India
Posted Date
Posted Date Today
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Customer Support English Communication Hindi Communication Semi Voice Blended Process Email Support Chat Support Voice Support CRM Windows 10 Internet Speed 30Mbps USB Headset Functional Web Camera i5 Processor
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Aptitude and Reasoning Practice Open Resource ↗
Helps sharpen logical thinking and problem‑solving skills essential for handling diverse customer queries.
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Interview Preparation for Customer Support Roles Open Resource ↗
Provides insights into common interview questions and scenarios for support positions.
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General Interview Preparation Open Resource ↗
Offers comprehensive guidance on preparing for interviews across various industries.

Degree: Any Graduate (any branch). Experience: 0‑5 years in BPO/Call Centre or similar environment. Language: Fluent in English (spoken & written) and Hindi (written & spoken). Technical: Proficiency in semi‑voice, blended, email, chat, and voice support. Minimum academic score: 50% or above. No backlog allowed.

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Round 1: HR/Recruiter screening
2
Round 2: Skills Assessment (call handling & communication test)
3
Round 3: HR interview
Teleperformance (TP) is a global digital business services leader that specializes in transforming operational challenges into high‑performance solutions. With nearly five decades of experience across five continents, TP partners with more than 400 clients in over 170 countries, delivering intelligent human‑AI operations at scale. The company’s culture is built on continuous improvement, innovation, and a commitment to responsible, reliable, and efficient service delivery. The Customer Support Executive – WFH English Hindi role is a full‑time, remote position based in Bengaluru. As a frontline support agent, you will provide exceptional customer service across multiple channels—phone, email, chat, and social media—handling domestic calls and queries in both English and Hindi. Your responsibilities include resolving product and service inquiries, maintaining accurate CRM records, and collaborating with internal teams to address complex issues. Key responsibilities: 1. Deliver high‑quality support via phone, email, chat, and social media. 2. Handle domestic calls and emails promptly and professionally. 3. Resolve customer queries related to TP’s products and services. 4. Record all interactions accurately in the CRM system. 5. Escalate unresolved issues to appropriate teams. 6. Maintain knowledge of product updates and service changes. 7. Meet or exceed key performance indicators such as response time and customer satisfaction. 8. Participate in training sessions to improve product knowledge and communication skills. 9. Adhere to company policies on data security and privacy. 10. Contribute to continuous improvement initiatives. Tech stack and tools: Windows 10 or above, CRM software, high‑speed internet (30 Mbps+), functional webcam, USB headset, and an Intel i5 processor. The role offers a clear growth path—from entry‑level support to senior specialist, team lead, or quality analyst positions—supported by TP’s global learning and development programs. Why join TP? You’ll work for a Fortune‑500‑listed company that values diversity, offers competitive compensation, and provides opportunities to work with cutting‑edge AI‑driven customer experience solutions. The remote work model ensures flexibility, while the company’s commitment to employee well‑being and career progression makes it an attractive destination for freshers and experienced professionals alike.

Teleperformance (TP) — AI & Machine Learning Interview Guide

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

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

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

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COMPANY GUIDE 🎯
Teleperformance (TP) Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Teleperformance (TP) and off-campus tech roles.

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MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

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

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CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

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

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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 (Customer Support, English Communication, Hindi Communication, Semi Voice, Blended Process, Email Support, Chat Support, Voice Support, CRM, Windows 10, Internet Speed 30Mbps, USB Headset, Functional Web Camera, i5 Processor) & 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
Teleperformance (TP) Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer ▼
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer ▼
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer ▼
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer ▼
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join Teleperformance (TP) as a Customer Support Executive - WFH English Hindi?
Preparation Tip: Highlight Teleperformance (TP)'s market reputation, recent tech innovations, and how your skills in Customer Support 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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